Christopher Boomhower1, Stacey Fabricant2, Alex Frye1, David Mumford2, Michael Smith1, Lindsay Vitovsky1
1 Southern Methodist University, Dallas, TX, US 2 Penn Mutual Life Insurance Co, Horsham PA </i></b>
To begin our analysis, we need to load the data from our 89 source .txt files. Data is separated into two separate groups of files; Separation and Non-Separation, thus data is loaded in two separate phases, then unioned together. Once data is loaded, Steps taken to remove non-US observations or those with no specified occupation, no specified salary, or no specified length of service level. Of a total 8,423,336 observations, we end with 8,232,375 after removal of these observations.
## Import libraries
import pickle
import os
import psutil
import glob
import pandas as pd
import numpy as np
from IPython.display import display
import matplotlib
import matplotlib.pyplot as plt
from matplotlib.colors import ListedColormap
import seaborn as sns
import requests
import json
import missingno as msno
import prettytable
import math
from sklearn.preprocessing import MinMaxScaler, StandardScaler, label_binarize
from sklearn.multiclass import OneVsRestClassifier
from sklearn.utils import class_weight
from sklearn.decomposition import PCA
from sklearn.pipeline import Pipeline
from sklearn.model_selection import StratifiedKFold
from sklearn.cross_validation import cross_val_score
from sklearn.metrics import roc_curve, auc
from sklearn.metrics import roc_auc_score
from scipy import interp
from sklearn.metrics import confusion_matrix
from sklearn.ensemble import RandomForestClassifier
from sklearn.neighbors import KNeighborsClassifier
from sklearn.linear_model import LogisticRegression
from sklearn.cross_validation import ShuffleSplit
from sklearn.metrics import log_loss
from sklearn.metrics import roc_auc_score
from datetime import datetime
from dateutil.parser import parse
from itertools import cycle
from sklearn import metrics as mt
from sklearn.feature_selection import chi2
import itertools
#Need to make sure you install the rpy2 package via following command in the Putty genuse41 console:
#python3 /usr/bin/pip install --user rpy2
#NOTE: If the above pip install does not work, try the following instead:
#python3 /usr/local/es7/lib/python3.5/site-packages/pip install --user rpy2
%load_ext rpy2.ipython
from rpy2.robjects import pandas2ri
## Library Options
pd.options.mode.chained_assignment = None
pd.set_option('display.max_rows', 500)
pd.set_option('display.max_columns', 500)
pd.set_option('display.width', 1000)
/usr/local/es7/lib/python3.5/site-packages/sklearn/cross_validation.py:44: DeprecationWarning: This module was deprecated in version 0.18 in favor of the model_selection module into which all the refactored classes and functions are moved. Also note that the interface of the new CV iterators are different from that of this module. This module will be removed in 0.20. "This module will be removed in 0.20.", DeprecationWarning)
## Pre-defined Functions for use later
def pickleObject(objectname, filename, filepath = "PickleJar/"):
fullpicklepath = "{0}{1}.pkl".format(filepath, filename)
# Create a variable to pickle and open it in write mode
picklefile = open(fullpicklepath, 'wb')
pickle.dump(objectname, picklefile)
picklefile.close()
def unpickleObject(filename, filepath = "PickleJar/"):
fullunpicklepath = "{0}{1}.pkl".format(filepath, filename)
# Create an variable to pickle and open it in write mode
unpicklefile = open(fullunpicklepath, 'rb')
unpickleObject = pickle.load(unpicklefile)
unpicklefile.close()
return unpickleObject
def clear_display():
from IPython.display import clear_output
## Pre-defined variables for use later
dataOPMPath = "dataOPM"
dataEMPPath = "dataEMP"
PickleJarPath = "PickleJar"
%%time
## Load OPMSeparation Files
OPMDataFiles = glob.glob(os.path.join(dataOPMPath, "*.txt"))
for i in range(0,len(OPMDataFiles)):
OPMDataFiles[i] = OPMDataFiles[i].replace("\\","/")
OPMDataList = []
for i,j in zip(OPMDataFiles,range(0,len(OPMDataFiles))):
OPMDataList.append(pd.read_csv(i, dtype = 'str'))
display(OPMDataList[j].head())
## Load the SEPDATA_FY2015 file into it's own object
indexes = [i for i,x in enumerate(OPMDataFiles) if x == 'dataOPM/SEPDATA_FY2015.txt']
OPMDataOrig = OPMDataList[indexes[0]]
| AGELVL | AGELVLT | |
|---|---|---|
| 0 | A | Less than 20 |
| 1 | B | 20-24 |
| 2 | C | 25-29 |
| 3 | D | 30-34 |
| 4 | E | 35-39 |
| AGYTYP | AGYTYPT | AGY | AGYT | AGYSUB | AGYSUBT | |
|---|---|---|---|---|---|---|
| 0 | 1 | Cabinet Level Agencies | AF | AF-DEPARTMENT OF THE AIR FORCE | AF** | AF**-INVALID |
| 1 | 1 | Cabinet Level Agencies | AF | AF-DEPARTMENT OF THE AIR FORCE | AF02 | AF02-AIR FORCE INSPECTION AGENCY (FO) |
| 2 | 1 | Cabinet Level Agencies | AF | AF-DEPARTMENT OF THE AIR FORCE | AF03 | AF03-AIR FORCE OPERATIONAL TEST AND EVALUATION... |
| 3 | 1 | Cabinet Level Agencies | AF | AF-DEPARTMENT OF THE AIR FORCE | AF06 | AF06-AIR FORCE AUDIT AGENCY |
| 4 | 1 | Cabinet Level Agencies | AF | AF-DEPARTMENT OF THE AIR FORCE | AF07 | AF07-AIR FORCE OFFICE OF SPECIAL INVESTIGATIONS |
| QTR | QTRT | EFDATE | EFDATET | |
|---|---|---|---|---|
| 0 | 1 | OCT-DEC 2014 | 201410 | OCT 2014 |
| 1 | 1 | OCT-DEC 2014 | 201411 | NOV 2014 |
| 2 | 1 | OCT-DEC 2014 | 201412 | DEC 2014 |
| 3 | 2 | JAN-MAR 2015 | 201501 | JAN 2015 |
| 4 | 2 | JAN-MAR 2015 | 201502 | FEB 2015 |
| GENDER | GENDERT | |
|---|---|---|
| 0 | F | Female |
| 1 | M | Male |
| 2 | Z | Unspecified |
| GSEGRD | |
|---|---|
| 0 | ** |
| 1 | 01 |
| 2 | 02 |
| 3 | 03 |
| 4 | 04 |
| LOCTYP | LOCTYPT | LOC | LOCT | |
|---|---|---|---|---|
| 0 | 1 | United States | 01 | 01-ALABAMA |
| 1 | 1 | United States | 02 | 02-ALASKA |
| 2 | 1 | United States | 04 | 04-ARIZONA |
| 3 | 1 | United States | 05 | 05-ARKANSAS |
| 4 | 1 | United States | 06 | 06-CALIFORNIA |
| LOSLVL | LOSLVLT | |
|---|---|---|
| 0 | A | Less than 1 year |
| 1 | B | 1 - 2 years |
| 2 | C | 3 - 4 years |
| 3 | D | 5 - 9 years |
| 4 | E | 10 - 14 years |
| OCCTYP | OCCTYPT | OCCFAM | OCCFAMT | OCC | OCCT | |
|---|---|---|---|---|---|---|
| 0 | 1 | White Collar | 00 | 00xx-MISCELLANEOUS OCCUPATIONS | 0006 | 0006-CORRECTIONAL INSTITUTION ADMINISTRATION |
| 1 | 1 | White Collar | 00 | 00xx-MISCELLANEOUS OCCUPATIONS | 0007 | 0007-CORRECTIONAL OFFICER |
| 2 | 1 | White Collar | 00 | 00xx-MISCELLANEOUS OCCUPATIONS | 0017 | 0017-EXPLOSIVES SAFETY |
| 3 | 1 | White Collar | 00 | 00xx-MISCELLANEOUS OCCUPATIONS | 0018 | 0018-SAFETY AND OCCUPATIONAL HEALTH MANAGEMENT |
| 4 | 1 | White Collar | 00 | 00xx-MISCELLANEOUS OCCUPATIONS | 0019 | 0019-SAFETY TECHNICIAN |
| PATCO | PATCOT | |
|---|---|---|
| 0 | 1 | Professional |
| 1 | 2 | Administrative |
| 2 | 3 | Technical |
| 3 | 4 | Clerical |
| 4 | 5 | Other White Collar |
| PPTYP | PPTYPT | PPGROUP | PPGROUPT | PAYPLAN | PAYPLANT | PPGRD | |
|---|---|---|---|---|---|---|---|
| 0 | 1 | General Schedule and Equivalently Graded (GSEG... | 11 | Standard GSEG Pay Plans | GL | GL-GS EMPLOYEES IN GRADES 3 THROUGH 10 PAID A ... | GL-03 |
| 1 | 1 | General Schedule and Equivalently Graded (GSEG... | 11 | Standard GSEG Pay Plans | GL | GL-GS EMPLOYEES IN GRADES 3 THROUGH 10 PAID A ... | GL-04 |
| 2 | 1 | General Schedule and Equivalently Graded (GSEG... | 11 | Standard GSEG Pay Plans | GL | GL-GS EMPLOYEES IN GRADES 3 THROUGH 10 PAID A ... | GL-05 |
| 3 | 1 | General Schedule and Equivalently Graded (GSEG... | 11 | Standard GSEG Pay Plans | GL | GL-GS EMPLOYEES IN GRADES 3 THROUGH 10 PAID A ... | GL-06 |
| 4 | 1 | General Schedule and Equivalently Graded (GSEG... | 11 | Standard GSEG Pay Plans | GL | GL-GS EMPLOYEES IN GRADES 3 THROUGH 10 PAID A ... | GL-07 |
| SALLVL | SALLVLT | |
|---|---|---|
| 0 | A | Less than $20,000 |
| 1 | B | $20,000 - $29,999 |
| 2 | C | $30,000 - $39,999 |
| 3 | D | $40,000 - $49,999 |
| 4 | E | $50,000 - $59,999 |
| SEP | SEPT | |
|---|---|---|
| 0 | SA | Transfer Out - Individual Transfer |
| 1 | SB | Transfer Out - Mass Transfer |
| 2 | SC | Quit |
| 3 | SD | Retirement - Voluntary |
| 4 | SE | Retirement - Early Out |
| TOATYP | TOATYPT | TOA | TOAT | |
|---|---|---|---|---|
| 0 | 1 | Permanent | 10 | 10-Competitive Service - Career |
| 1 | 1 | Permanent | 15 | 15-Competitive Service - Career-Conditional |
| 2 | 1 | Permanent | 30 | 30-Excepted Service - Schedule A |
| 3 | 1 | Permanent | 32 | 32-Excepted Service - Schedule B |
| 4 | 1 | Permanent | 34 | 34-Excepted Service - Schedule C |
| WSTYP | WSTYPT | WORKSCH | WORKSCHT | |
|---|---|---|---|---|
| 0 | 1 | Full-time | B | B-Full-time Nonseasonal Baylor Plan |
| 1 | 1 | Full-time | F | F-Full-time Nonseasonal |
| 2 | 1 | Full-time | G | G-Full-time Seasonal |
| 3 | 1 | Full-time | H | H-Full-time On-call |
| 4 | 2 | Not Full-time | I | I-Intermittent Nonseasonal |
| AGYSUB | SEP | EFDATE | AGELVL | GENDER | GSEGRD | LOSLVL | LOC | OCC | PATCO | PPGRD | SALLVL | TOA | WORKSCH | COUNT | SALARY | LOS | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | AA00 | SC | 201507 | C | M | 11 | A | 11 | 0905 | 1 | GS-11 | F | 40 | F | 1 | 063722 | 00.8 |
| 1 | AA00 | SD | 201509 | K | M | NaN | D | 11 | 0301 | 2 | EX-02 | Z | 46 | F | 1 | NaN | 08.1 |
| 2 | AA00 | SC | 201506 | D | F | 15 | C | 11 | 0905 | 1 | GS-15 | L | 30 | F | 1 | 126245 | 04.8 |
| 3 | AF** | SA | 201503 | H | M | 11 | C | 48 | 2210 | 2 | GS-11 | F | 10 | F | 1 | 066585 | 04.9 |
| 4 | AF02 | SD | 201506 | I | M | 15 | J | 35 | 0301 | 2 | GS-15 | O | 10 | F | 1 | 156737 | 39.8 |
CPU times: user 415 ms, sys: 39.1 ms, total: 454 ms Wall time: 456 ms
%%time
#print(OPMDataFiles)
print(len(OPMDataOrig))
##### Merge / Modify Codes / Aggregate Attributes to be more descriptive per the metadata files
OPMDataMerged = OPMDataOrig.copy()
##AGYSUB - AGYTYP, AGY
indexes = [i for i,x in enumerate(OPMDataFiles) if x == 'dataOPM/DTagy.txt']
OPMDataMerged = OPMDataMerged.merge(OPMDataList[indexes[0]], on = 'AGYSUB', how = 'left')
##EFDate - quarter, month
indexes = [i for i,x in enumerate(OPMDataFiles) if x == 'dataOPM/DTefdate.txt']
OPMDataMerged = OPMDataMerged.merge(OPMDataList[indexes[0]], on = 'EFDATE', how = 'left')
##AGELVL - AGELVLT
indexes = [i for i,x in enumerate(OPMDataFiles) if x == 'dataOPM/DTagelvl.txt']
OPMDataMerged = OPMDataMerged.merge(OPMDataList[indexes[0]], on = 'AGELVL', how = 'left')
##LOSLVL - LOSLVLT
indexes = [i for i,x in enumerate(OPMDataFiles) if x == 'dataOPM/DTloslvl.txt']
OPMDataMerged = OPMDataMerged.merge(OPMDataList[indexes[0]], on = 'LOSLVL', how = 'left')
##LOC - LocTypeT, LocT
indexes = [i for i,x in enumerate(OPMDataFiles) if x == 'dataOPM/DTloc.txt']
OPMDataMerged = OPMDataMerged.merge(OPMDataList[indexes[0]], on = 'LOC', how = 'left')
##OCC - OCCTYPT, OCCFAM
indexes = [i for i,x in enumerate(OPMDataFiles) if x == 'dataOPM/DTocc.txt']
OPMDataMerged = OPMDataMerged.merge(OPMDataList[indexes[0]], on = 'OCC', how = 'left')
##PATCO - PATCOT
indexes = [i for i,x in enumerate(OPMDataFiles) if x == 'dataOPM/DTpatco.txt']
OPMDataMerged = OPMDataMerged.merge(OPMDataList[indexes[0]], on = 'PATCO', how = 'left')
##PPGRD - PayPlan, PPGroup, PPTYP
indexes = [i for i,x in enumerate(OPMDataFiles) if x == 'dataOPM/DTppgrd.txt']
OPMDataMerged = OPMDataMerged.merge(OPMDataList[indexes[0]], on = 'PPGRD', how = 'left')
##SALLVL - SALLVLT
indexes = [i for i,x in enumerate(OPMDataFiles) if x == 'dataOPM/DTsallvl.txt']
OPMDataMerged = OPMDataMerged.merge(OPMDataList[indexes[0]], on = 'SALLVL', how = 'left')
##TOA - TOATYP
indexes = [i for i,x in enumerate(OPMDataFiles) if x == 'dataOPM/DTtoa.txt']
OPMDataMerged = OPMDataMerged.merge(OPMDataList[indexes[0]], on = 'TOA', how = 'left')
##WORKSCH - WSTYPT
indexes = [i for i,x in enumerate(OPMDataFiles) if x == 'dataOPM/DTwrksch.txt']
OPMDataMerged = OPMDataMerged.merge(OPMDataList[indexes[0]], on = 'WORKSCH', how = 'left')
## Modify Data Types for numeric objects
OPMDataMerged["SALARY"] = OPMDataMerged["SALARY"].apply(pd.to_numeric)
OPMDataMerged["COUNT"] = OPMDataMerged["COUNT"].apply(pd.to_numeric)
OPMDataMerged["LOS"] = OPMDataMerged["LOS"].apply(pd.to_numeric)
print("Original SEP data size of: "+str(len(OPMDataMerged)))
print("Removing "+str(len(OPMDataMerged[OPMDataMerged["LOCTYP"] != "1"]))+" Non-US observations.")
## Remove Non-US Data
OPMDataMerged = OPMDataMerged[OPMDataMerged["LOCTYP"] == "1"]
print("Removing "+str(len(OPMDataMerged[OPMDataMerged["OCCTYP"] == "3"]))+" observations with no specified Occupation.")
## Remove Observations with no specified occupation
OPMDataMerged = OPMDataMerged[OPMDataMerged["OCCTYP"] != "3"]
print("Removing "+str(len(OPMDataMerged[OPMDataMerged["SALLVL"] == "Z"]))+" observations with no specified Salary.")
## Remove Observations with no specified salary
OPMDataMerged = OPMDataMerged[OPMDataMerged["SALLVL"] != "Z"]
print("Removing "+str(len(OPMDataMerged[OPMDataMerged["LOSLVL"] == "Z"]))+" observations with no specified Length of Service.")
## Remove Observations with no specified LOSLVL
OPMDataMerged = OPMDataMerged[OPMDataMerged["LOSLVL"] != "Z"]
print("Removing "+str(len(OPMDataMerged[OPMDataMerged["AGELVL"] == "A"]))+" observations of Age Level A")
## Remove Observations from Age Level A (less than 20 years old)
OPMDataMerged = OPMDataMerged[OPMDataMerged["AGELVL"] != "A"]
print("Removing "+str(len(OPMDataMerged[OPMDataMerged["AGELVL"] == "Z"]))+" observations with no specified Age Level.")
## Remove Observations with no specified Age Level
OPMDataMerged = OPMDataMerged[OPMDataMerged["AGELVL"] != "Z"]
## Fix differences in spaces on WORKSCHT Column
OPMDataMerged["WORKSCHT"] = np.where(OPMDataMerged["WORKSCHT"].str[0]=="F", 'Full-time Nonseasonal',
np.where(OPMDataMerged["WORKSCHT"].str[0]=="I", 'Intermittent Nonseasonal',
np.where(OPMDataMerged["WORKSCHT"].str[0]=="P", 'Part-time Nonseasonal',
np.where(OPMDataMerged["WORKSCHT"].str[0]=="G", 'Full-time Seasonal',
np.where(OPMDataMerged["WORKSCHT"].str[0]=="J", 'Intermittent Seasonal',
np.where(OPMDataMerged["WORKSCHT"].str[0]=="Q", 'Part-time Seasonal',
np.where(OPMDataMerged["WORKSCHT"].str[0]=="T", 'Part-time Job Sharer Seasonal',
np.where(OPMDataMerged["WORKSCHT"].str[0]=="S", 'Part-time Job Sharer Nonseasonal',
np.where(OPMDataMerged["WORKSCHT"].str[0]=="B", 'Full-time Nonseasonal Baylor Plan',
'NO WORK SCHEDULE REPORTED' ### ELSE case represents Night
)
)
)
)
)
)
)
)
)
display(OPMDataMerged.head())
print("New SEP data size of: "+str(len(OPMDataMerged)))
display(OPMDataMerged.describe().transpose())
#del OPMDataList,OPMDataFiles
226357 Original SEP data size of: 226357 Removing 8021 Non-US observations. Removing 55 observations with no specified Occupation. Removing 1426 observations with no specified Salary. Removing 3 observations with no specified Length of Service. Removing 2570 observations of Age Level A Removing 0 observations with no specified Age Level.
| AGYSUB | SEP | EFDATE | AGELVL | GENDER | GSEGRD | LOSLVL | LOC | OCC | PATCO | PPGRD | SALLVL | TOA | WORKSCH | COUNT | SALARY | LOS | AGYTYP | AGYTYPT | AGY | AGYT | AGYSUBT | QTR | QTRT | EFDATET | AGELVLT | LOSLVLT | LOCTYP | LOCTYPT | LOCT | OCCTYP | OCCTYPT | OCCFAM | OCCFAMT | OCCT | PATCOT | PPTYP | PPTYPT | PPGROUP | PPGROUPT | PAYPLAN | PAYPLANT | SALLVLT | TOATYP | TOATYPT | TOAT | WSTYP | WSTYPT | WORKSCHT | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | AA00 | SC | 201507 | C | M | 11 | A | 11 | 0905 | 1 | GS-11 | F | 40 | F | 1 | 63722.0 | 0.8 | 4 | Small Independent Agencies (less than 100 empl... | AA | AA-ADMINISTRATIVE CONFERENCE OF THE UNITED STATES | AA00-ADMINISTRATIVE CONFERENCE OF THE UNITED S... | 4 | JUL-SEP 2015 | JUL 2015 | 25-29 | Less than 1 year | 1 | United States | 11-DISTRICT OF COLUMBIA | 1 | White Collar | 09 | 09xx-LEGAL AND KINDRED | 0905-GENERAL ATTORNEY | Professional | 1 | General Schedule and Equivalently Graded (GSEG... | 11 | Standard GSEG Pay Plans | GS | GS-GENERAL SCHEDULE | $60,000 - $69,999 | 2 | Non-permanent | 40-Excepted Service - Schedule A | 1 | Full-time | Full-time Nonseasonal |
| 2 | AA00 | SC | 201506 | D | F | 15 | C | 11 | 0905 | 1 | GS-15 | L | 30 | F | 1 | 126245.0 | 4.8 | 4 | Small Independent Agencies (less than 100 empl... | AA | AA-ADMINISTRATIVE CONFERENCE OF THE UNITED STATES | AA00-ADMINISTRATIVE CONFERENCE OF THE UNITED S... | 3 | APR-JUN 2015 | JUN 2015 | 30-34 | 3 - 4 years | 1 | United States | 11-DISTRICT OF COLUMBIA | 1 | White Collar | 09 | 09xx-LEGAL AND KINDRED | 0905-GENERAL ATTORNEY | Professional | 1 | General Schedule and Equivalently Graded (GSEG... | 11 | Standard GSEG Pay Plans | GS | GS-GENERAL SCHEDULE | $120,000 - $129,999 | 1 | Permanent | 30-Excepted Service - Schedule A | 1 | Full-time | Full-time Nonseasonal |
| 3 | AF** | SA | 201503 | H | M | 11 | C | 48 | 2210 | 2 | GS-11 | F | 10 | F | 1 | 66585.0 | 4.9 | 1 | Cabinet Level Agencies | AF | AF-DEPARTMENT OF THE AIR FORCE | AF**-INVALID | 2 | JAN-MAR 2015 | MAR 2015 | 50-54 | 3 - 4 years | 1 | United States | 48-TEXAS | 1 | White Collar | 22 | 22xx-INFORMATION TECHNOLOGY | 2210-INFORMATION TECHNOLOGY MANAGEMENT | Administrative | 1 | General Schedule and Equivalently Graded (GSEG... | 11 | Standard GSEG Pay Plans | GS | GS-GENERAL SCHEDULE | $60,000 - $69,999 | 1 | Permanent | 10-Competitive Service - Career | 1 | Full-time | Full-time Nonseasonal |
| 4 | AF02 | SD | 201506 | I | M | 15 | J | 35 | 0301 | 2 | GS-15 | O | 10 | F | 1 | 156737.0 | 39.8 | 1 | Cabinet Level Agencies | AF | AF-DEPARTMENT OF THE AIR FORCE | AF02-AIR FORCE INSPECTION AGENCY (FO) | 3 | APR-JUN 2015 | JUN 2015 | 55-59 | 35 years or more | 1 | United States | 35-NEW MEXICO | 1 | White Collar | 03 | 03xx-GENERAL ADMIN, CLERICAL, & OFFICE SVCS | 0301-MISCELLANEOUS ADMINISTRATION AND PROGRAM | Administrative | 1 | General Schedule and Equivalently Graded (GSEG... | 11 | Standard GSEG Pay Plans | GS | GS-GENERAL SCHEDULE | $150,000 - $159,999 | 1 | Permanent | 10-Competitive Service - Career | 1 | Full-time | Full-time Nonseasonal |
| 5 | AF03 | SC | 201509 | H | M | 13 | B | 06 | 0301 | 2 | GS-13 | I | 15 | F | 1 | 92973.0 | 1.0 | 1 | Cabinet Level Agencies | AF | AF-DEPARTMENT OF THE AIR FORCE | AF03-AIR FORCE OPERATIONAL TEST AND EVALUATION... | 4 | JUL-SEP 2015 | SEP 2015 | 50-54 | 1 - 2 years | 1 | United States | 06-CALIFORNIA | 1 | White Collar | 03 | 03xx-GENERAL ADMIN, CLERICAL, & OFFICE SVCS | 0301-MISCELLANEOUS ADMINISTRATION AND PROGRAM | Administrative | 1 | General Schedule and Equivalently Graded (GSEG... | 11 | Standard GSEG Pay Plans | GS | GS-GENERAL SCHEDULE | $90,000 - $99,999 | 1 | Permanent | 15-Competitive Service - Career-Conditional | 1 | Full-time | Full-time Nonseasonal |
New SEP data size of: 214282
| count | mean | std | min | 25% | 50% | 75% | max | |
|---|---|---|---|---|---|---|---|---|
| COUNT | 214282.0 | 1.000000 | 0.000000 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 |
| SALARY | 214282.0 | 66479.453855 | 39471.623281 | 3913.0 | 35830.0 | 54424.0 | 86910.0 | 393699.0 |
| LOS | 214282.0 | 11.708865 | 12.631714 | 0.0 | 1.3 | 6.2 | 20.4 | 71.5 |
CPU times: user 14.5 s, sys: 203 ms, total: 14.7 s Wall time: 14.7 s
%%time
if os.path.isfile(PickleJarPath+"/EMPDataOrig4Q.pkl"):
print("Found the File! Loading Pickle Now!")
EMPDataOrig4Q = unpickleObject("EMPDataOrig4Q")
else:
## Load EMPData Files
indexes = []
EMPDataFiles = []
EMPDataList = []
EMPDataOrig = []
for i,qtr in enumerate(["Q1", "Q2", "Q3", "Q4"]):
EMPDataFiles.append(glob.glob(os.path.join(dataEMPPath, qtr + "/*.txt")))
for j in range(0,len(EMPDataFiles[i])):
EMPDataFiles[i][j] = EMPDataFiles[i][j].replace("\\","/")
EMPDataList.append([])
for j,file in enumerate(EMPDataFiles[i]):
EMPDataList[i].append(pd.read_csv(file, dtype = 'str'))
if i == 0:
display(EMPDataList[i][j].head())
## Load the FactData files into it's own object
indexes.append([])
##[qtr][fileindex from EMPDataList]
indexes[i]=[j for j,x in enumerate(EMPDataFiles[i]) if dataEMPPath + '/' + qtr + '/FACTDATA' in x]
EMPDataOrig.append([])
EMPDataOrig[i] = pd.concat([EMPDataList[i][indexes[i][j]] for j in range(0,len(indexes[i]))])
EMPDataOrig[i]["QTR"] = str(i+1)
## modify data type for numerics
EMPDataOrig[i]["SALARY"] = EMPDataOrig[i]["SALARY"].str.replace(',', '').str.replace('$', '').str.replace(' ', '').apply(pd.to_numeric)
## Load Metadata
##AGYSUB - AGYTYP, AGY
ind2 = [i for i,x in enumerate(EMPDataFiles[i]) if x == dataEMPPath + '/' + qtr + '/DTagy.txt']
EMPDataOrig[i] = EMPDataOrig[i].merge(EMPDataList[i][ind2[0]], on = 'AGYSUB', how = 'left')
##AGELVL - AGELVLT
ind2 = [i for i,x in enumerate(EMPDataFiles[i]) if x == dataEMPPath + '/' + qtr + '/DTagelvl.txt']
EMPDataOrig[i] = EMPDataOrig[i].merge(EMPDataList[i][ind2[0]], on = 'AGELVL', how = 'left')
#LOSLVL - LOSLVLT
ind2 = [i for i,x in enumerate(EMPDataFiles[i]) if x == dataEMPPath + '/' + qtr + '/DTloslvl.txt']
EMPDataOrig[i] = EMPDataOrig[i].merge(EMPDataList[i][ind2[0]], on = 'LOSLVL', how = 'left')
EMPDataOrig[i]["LOS"] = EMPDataOrig[i]["LOS"].apply(pd.to_numeric)
##LOC - LocTypeT, LocT
ind2 = [i for i,x in enumerate(EMPDataFiles[i]) if x == dataEMPPath + '/' + qtr + '/DTloc.txt']
EMPDataOrig[i] = EMPDataOrig[i].merge(EMPDataList[i][ind2[0]], on = 'LOC', how = 'left')
##OCC - OCCTYPT, OCCFAM
ind2 = [i for i,x in enumerate(EMPDataFiles[i]) if x == dataEMPPath + '/' + qtr + '/DTocc.txt']
EMPDataOrig[i] = EMPDataOrig[i].merge(EMPDataList[i][ind2[0]], on = 'OCC', how = 'left')
##PATCO - PATCOT
ind2 = [i for i,x in enumerate(EMPDataFiles[i]) if x == dataEMPPath + '/' + qtr + '/DTpatco.txt']
EMPDataOrig[i] = EMPDataOrig[i].merge(EMPDataList[i][ind2[0]], on = 'PATCO', how = 'left')
##PPGRD - PayPlan, PPGroup, PPTYP
ind2 = [i for i,x in enumerate(EMPDataFiles[i]) if x == dataEMPPath + '/' + qtr + '/DTppgrd.txt']
EMPDataOrig[i] = EMPDataOrig[i].merge(EMPDataList[i][ind2[0]], on = 'PPGRD', how = 'left')
##SALLVL - SALLVLT
ind2 = [i for i,x in enumerate(EMPDataFiles[i]) if x == dataEMPPath + '/' + qtr + '/DTsallvl.txt']
EMPDataOrig[i] = EMPDataOrig[i].merge(EMPDataList[i][ind2[0]], on = 'SALLVL', how = 'left')
##TOA - TOATYP
ind2 = [i for i,x in enumerate(EMPDataFiles[i]) if x == dataEMPPath + '/' + qtr + '/DTtoa.txt']
EMPDataOrig[i] = EMPDataOrig[i].merge(EMPDataList[i][ind2[0]], on = 'TOA', how = 'left')
##WORKSCH - WSTYPT
ind2 = [i for i,x in enumerate(EMPDataFiles[i]) if x == dataEMPPath + '/' + qtr + '/DTwrksch.txt']
EMPDataOrig[i] = EMPDataOrig[i].merge(EMPDataList[i][ind2[0]], on = 'WORKSCH', how = 'left')
display(EMPDataOrig[i].head())
EMPDataOrig4Q = pd.concat([EMPDataOrig[j] for j in range(0,len(EMPDataOrig))])
print("Original EMP data size of: "+str(len(EMPDataOrig4Q)))
print("Removing "+str(len(EMPDataOrig4Q[EMPDataOrig4Q["LOCTYP"] != "1"]))+" Non-US observations.")
## Remove Non-US Data
EMPDataOrig4Q = EMPDataOrig4Q[EMPDataOrig4Q["LOCTYP"] == "1"]
print("Removing "+str(len(EMPDataOrig4Q[EMPDataOrig4Q["OCCTYP"] == "3"]))+" observations with no specified Occupation.")
## Remove Observations with no specified occupation
EMPDataOrig4Q = EMPDataOrig4Q[EMPDataOrig4Q["OCCTYP"] != "3"]
print("Removing "+str(len(EMPDataOrig4Q[EMPDataOrig4Q["SALLVL"] == "Z"]))+" observations with no specified Salary.")
## Remove Observations with no specified salary
EMPDataOrig4Q = EMPDataOrig4Q[EMPDataOrig4Q["SALLVL"] != "Z"]
print("Removing "+str(len(EMPDataOrig4Q[EMPDataOrig4Q["LOSLVL"] == "Z"]))+" observations with no specified Length of Service.")
## Remove Observations with no specified LOSLVL
EMPDataOrig4Q = EMPDataOrig4Q[EMPDataOrig4Q["LOSLVL"] != "Z"]
print("Removing "+str(len(EMPDataOrig4Q[EMPDataOrig4Q["AGELVL"] == "A"]))+" observations of Age Level A.")
## Remove Observations from Age Level A (less than 20 years old)
EMPDataOrig4Q = EMPDataOrig4Q[EMPDataOrig4Q["AGELVL"] != "A"]
print("Removing "+str(len(EMPDataOrig4Q[EMPDataOrig4Q["AGELVL"] == "Z"]))+" observations with no specified Age Level.")
## Remove Observations with no specified Age Level
EMPDataOrig4Q = EMPDataOrig4Q[EMPDataOrig4Q["AGELVL"] != "Z"]
## Fix differences in spaces on WORKSCHT Column
EMPDataOrig4Q["WORKSCHT"] = np.where(EMPDataOrig4Q["WORKSCHT"].str[0]=="F", 'Full-time Nonseasonal',
np.where(EMPDataOrig4Q["WORKSCHT"].str[0]=="I", 'Intermittent Nonseasonal',
np.where(EMPDataOrig4Q["WORKSCHT"].str[0]=="P", 'Part-time Nonseasonal',
np.where(EMPDataOrig4Q["WORKSCHT"].str[0]=="G", 'Full-time Seasonal',
np.where(EMPDataOrig4Q["WORKSCHT"].str[0]=="J", 'Intermittent Seasonal',
np.where(EMPDataOrig4Q["WORKSCHT"].str[0]=="Q", 'Part-time Seasonal',
np.where(EMPDataOrig4Q["WORKSCHT"].str[0]=="T", 'Part-time Job Sharer Seasonal',
np.where(EMPDataOrig4Q["WORKSCHT"].str[0]=="S", 'Part-time Job Sharer Nonseasonal',
np.where(EMPDataOrig4Q["WORKSCHT"].str[0]=="B", 'Full-time Nonseasonal Baylor Plan',
'NO WORK SCHEDULE REPORTED' ### ELSE case represents Night
)
)
)
)
)
)
)
)
)
pickleObject(EMPDataOrig4Q, "EMPDataOrig4Q")
print("New EMP data size of: "+str(len(EMPDataOrig4Q)))
Found the File! Loading Pickle Now! New EMP data size of: 8008911 CPU times: user 9.06 s, sys: 1.76 s, total: 10.8 s Wall time: 10.8 s
display(EMPDataOrig4Q.describe().transpose())
| count | mean | std | min | 25% | 50% | 75% | max | |
|---|---|---|---|---|---|---|---|---|
| SALARY | 8008911.0 | 80067.37279 | 37918.758366 | 15120.0 | 51437.0 | 74130.0 | 99957.0 | 401589.0 |
| LOS | 8008911.0 | 13.06029 | 10.446755 | 0.0 | 4.9 | 10.0 | 20.1 | 71.1 |
%matplotlib inline
#sns.boxplot(y = "SALARY", data = EMPDataOrig4Q)
With both our separation and non-separation data loaded, we calculate three new attributes through aggregation or calculation amongst various attributes.
1) SEP Count by Date & Occupation – total number of separations (of any type) for a given Date and Occupation;
2) SEP Count by Date & Location – total number of separations (of any type) for a given Date and Location;
3) Industry Average Salary – Average salary amongst non-separated employees, grouped by quarter, occupation, pay grade, and work schedule;
We proceed, by concatenating our Separation and Non-Separation observations, and merge these newly calculated attributes to the concatenated dataset.
%%time
%matplotlib inline
##Aggregate Number of Total Separations in current month for given Occ
AggSEPCount_EFDATE_OCC= pd.DataFrame({'SEPCount_EFDATE_OCC' : OPMDataMerged.groupby(["EFDATE", "OCC"]).size()}).reset_index()
display(AggSEPCount_EFDATE_OCC.head())
##Aggregate Number of Total Separations in current month for given LOC
AggSEPCount_EFDATE_LOC = pd.DataFrame({'SEPCount_EFDATE_LOC' : OPMDataMerged.groupby(["EFDATE", "LOC"]).size()}).reset_index()
display(AggSEPCount_EFDATE_LOC.head())
##Average Quarterly EMP Salary by occ
AggIndAvgSalary = pd.DataFrame({'count' : EMPDataOrig4Q.groupby(["QTR", "OCC", "PPGRD", "WORKSCHT"]).size()}).reset_index()
AggIndAvgSalary2 = pd.DataFrame({'IndSalarySum' : EMPDataOrig4Q.groupby(["QTR", "OCC", "PPGRD", "WORKSCHT"])["SALARY"].sum()}).reset_index()
AggIndAvgSalary = AggIndAvgSalary.merge(AggIndAvgSalary2,on=["QTR", "OCC", "PPGRD", "WORKSCHT"])
AggIndAvgSalary["IndAvgSalary"] = AggIndAvgSalary["IndSalarySum"]/AggIndAvgSalary["count"]
del AggIndAvgSalary["count"]
del AggIndAvgSalary["IndSalarySum"]
display(AggIndAvgSalary.head())
| EFDATE | OCC | SEPCount_EFDATE_OCC | |
|---|---|---|---|
| 0 | 201410 | 0006 | 20 |
| 1 | 201410 | 0007 | 89 |
| 2 | 201410 | 0017 | 1 |
| 3 | 201410 | 0018 | 33 |
| 4 | 201410 | 0019 | 1 |
| EFDATE | LOC | SEPCount_EFDATE_LOC | |
|---|---|---|---|
| 0 | 201410 | 01 | 239 |
| 1 | 201410 | 02 | 261 |
| 2 | 201410 | 04 | 499 |
| 3 | 201410 | 05 | 132 |
| 4 | 201410 | 06 | 1926 |
| QTR | OCC | PPGRD | WORKSCHT | IndAvgSalary | |
|---|---|---|---|---|---|
| 0 | 1 | 0006 | ES-** | Full-time Nonseasonal | 161827.273973 |
| 1 | 1 | 0006 | GL-09 | Full-time Nonseasonal | 63970.126984 |
| 2 | 1 | 0006 | GS-09 | Full-time Nonseasonal | 56876.500000 |
| 3 | 1 | 0006 | GS-11 | Full-time Nonseasonal | 72865.783673 |
| 4 | 1 | 0006 | GS-12 | Full-time Nonseasonal | 85742.663717 |
CPU times: user 3.02 s, sys: 465 ms, total: 3.48 s Wall time: 3.48 s
#Merge Two Datasets
### NS SEP code means NonSeparation
###add hardcoded null value columns where applicable
EMPDataOrig4Q["SEP"] = "NS"
EMPDataOrig4Q["GENDER"] = np.nan
EMPDataOrig4Q["COUNT"] = np.nan
OPMDataMerged["DATECODE"] = OPMDataMerged["EFDATE"]
OPMColList = ["AGYSUB", "SEP", "DATECODE", "AGELVL", "GENDER", "GSEGRD", "LOSLVL", "LOC", "OCC", "PATCO", "PPGRD", "SALLVL", "TOA", "WORKSCH", "COUNT", "SALARY", "LOS", "AGYTYP", "AGYTYPT", "AGY", "AGYT", "AGYSUBT", "QTR", "AGELVLT", "LOSLVLT", "LOCTYP", "LOCTYPT", "LOCT", "OCCTYP", "OCCTYPT", "OCCFAM", "OCCFAMT", "OCCT", "PATCOT", "PPTYP", "PPTYPT", "PPGROUP", "PPGROUPT", "PAYPLAN", "PAYPLANT", "SALLVLT", "TOATYP", "TOATYPT", "TOAT", "WSTYP", "WSTYPT", "WORKSCHT"]
EMPColList = ["AGYSUB", "SEP", "DATECODE", "AGELVL", "GENDER", "GSEGRD", "LOSLVL", "LOC", "OCC", "PATCO", "PPGRD", "SALLVL", "TOA", "WORKSCH", "COUNT", "SALARY", "LOS", "AGYTYP", "AGYTYPT", "AGY", "AGYT", "AGYSUBT", "QTR", "AGELVLT", "LOSLVLT", "LOCTYP", "LOCTYPT", "LOCT", "OCCTYP", "OCCTYPT", "OCCFAM", "OCCFAMT", "OCCT", "PATCOT", "PPTYP", "PPTYPT", "PPGROUP", "PPGROUPT", "PAYPLAN", "PAYPLANT", "SALLVLT", "TOATYP", "TOATYPT", "TOAT", "WSTYP", "WSTYPT", "WORKSCHT"]
OPMDataMerged = pd.concat([OPMDataMerged[OPMColList], EMPDataOrig4Q[EMPColList]], ignore_index=True)
print("Total concatenated data size for SEP and non-SEP: "+str(len(OPMDataMerged)))
OPMDataMerged = OPMDataMerged.merge(AggSEPCount_EFDATE_OCC, left_on = ['DATECODE','OCC'], right_on = ['EFDATE','OCC'], how = 'left')
OPMDataMerged = OPMDataMerged.merge(AggSEPCount_EFDATE_LOC, left_on = ['DATECODE','LOC'], right_on = ['EFDATE','LOC'], how = 'left')
OPMDataMerged = OPMDataMerged.merge(AggIndAvgSalary, on = ['QTR','OCC', 'PPGRD', 'WORKSCHT'], how = 'left')
OPMDataMerged["SalaryOverUnderIndAvg"] = OPMDataMerged["SALARY"] - OPMDataMerged["IndAvgSalary"]
del OPMDataMerged["EFDATE_x"]
del OPMDataMerged["EFDATE_y"]
display(OPMDataMerged.head())
display(OPMDataMerged.tail())
Total concatenated data size for SEP and non-SEP: 8223193
| AGYSUB | SEP | DATECODE | AGELVL | GENDER | GSEGRD | LOSLVL | LOC | OCC | PATCO | PPGRD | SALLVL | TOA | WORKSCH | COUNT | SALARY | LOS | AGYTYP | AGYTYPT | AGY | AGYT | AGYSUBT | QTR | AGELVLT | LOSLVLT | LOCTYP | LOCTYPT | LOCT | OCCTYP | OCCTYPT | OCCFAM | OCCFAMT | OCCT | PATCOT | PPTYP | PPTYPT | PPGROUP | PPGROUPT | PAYPLAN | PAYPLANT | SALLVLT | TOATYP | TOATYPT | TOAT | WSTYP | WSTYPT | WORKSCHT | SEPCount_EFDATE_OCC | SEPCount_EFDATE_LOC | IndAvgSalary | SalaryOverUnderIndAvg | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | AA00 | SC | 201507 | C | M | 11 | A | 11 | 0905 | 1 | GS-11 | F | 40 | F | 1.0 | 63722.0 | 0.8 | 4 | Small Independent Agencies (less than 100 empl... | AA | AA-ADMINISTRATIVE CONFERENCE OF THE UNITED STATES | AA00-ADMINISTRATIVE CONFERENCE OF THE UNITED S... | 4 | 25-29 | Less than 1 year | 1 | United States | 11-DISTRICT OF COLUMBIA | 1 | White Collar | 09 | 09xx-LEGAL AND KINDRED | 0905-GENERAL ATTORNEY | Professional | 1 | General Schedule and Equivalently Graded (GSEG... | 11 | Standard GSEG Pay Plans | GS | GS-GENERAL SCHEDULE | $60,000 - $69,999 | 2 | Non-permanent | 40-Excepted Service - Schedule A | 1 | Full-time | Full-time Nonseasonal | 205.0 | 1319 | 64540.593830 | -818.593830 |
| 1 | AA00 | SC | 201506 | D | F | 15 | C | 11 | 0905 | 1 | GS-15 | L | 30 | F | 1.0 | 126245.0 | 4.8 | 4 | Small Independent Agencies (less than 100 empl... | AA | AA-ADMINISTRATIVE CONFERENCE OF THE UNITED STATES | AA00-ADMINISTRATIVE CONFERENCE OF THE UNITED S... | 3 | 30-34 | 3 - 4 years | 1 | United States | 11-DISTRICT OF COLUMBIA | 1 | White Collar | 09 | 09xx-LEGAL AND KINDRED | 0905-GENERAL ATTORNEY | Professional | 1 | General Schedule and Equivalently Graded (GSEG... | 11 | Standard GSEG Pay Plans | GS | GS-GENERAL SCHEDULE | $120,000 - $129,999 | 1 | Permanent | 30-Excepted Service - Schedule A | 1 | Full-time | Full-time Nonseasonal | 207.0 | 1132 | 149864.298504 | -23619.298504 |
| 2 | AF** | SA | 201503 | H | M | 11 | C | 48 | 2210 | 2 | GS-11 | F | 10 | F | 1.0 | 66585.0 | 4.9 | 1 | Cabinet Level Agencies | AF | AF-DEPARTMENT OF THE AIR FORCE | AF**-INVALID | 2 | 50-54 | 3 - 4 years | 1 | United States | 48-TEXAS | 1 | White Collar | 22 | 22xx-INFORMATION TECHNOLOGY | 2210-INFORMATION TECHNOLOGY MANAGEMENT | Administrative | 1 | General Schedule and Equivalently Graded (GSEG... | 11 | Standard GSEG Pay Plans | GS | GS-GENERAL SCHEDULE | $60,000 - $69,999 | 1 | Permanent | 10-Competitive Service - Career | 1 | Full-time | Full-time Nonseasonal | 439.0 | 1087 | 71530.963755 | -4945.963755 |
| 3 | AF02 | SD | 201506 | I | M | 15 | J | 35 | 0301 | 2 | GS-15 | O | 10 | F | 1.0 | 156737.0 | 39.8 | 1 | Cabinet Level Agencies | AF | AF-DEPARTMENT OF THE AIR FORCE | AF02-AIR FORCE INSPECTION AGENCY (FO) | 3 | 55-59 | 35 years or more | 1 | United States | 35-NEW MEXICO | 1 | White Collar | 03 | 03xx-GENERAL ADMIN, CLERICAL, & OFFICE SVCS | 0301-MISCELLANEOUS ADMINISTRATION AND PROGRAM | Administrative | 1 | General Schedule and Equivalently Graded (GSEG... | 11 | Standard GSEG Pay Plans | GS | GS-GENERAL SCHEDULE | $150,000 - $159,999 | 1 | Permanent | 10-Competitive Service - Career | 1 | Full-time | Full-time Nonseasonal | 670.0 | 265 | 146735.220304 | 10001.779696 |
| 4 | AF03 | SC | 201509 | H | M | 13 | B | 06 | 0301 | 2 | GS-13 | I | 15 | F | 1.0 | 92973.0 | 1.0 | 1 | Cabinet Level Agencies | AF | AF-DEPARTMENT OF THE AIR FORCE | AF03-AIR FORCE OPERATIONAL TEST AND EVALUATION... | 4 | 50-54 | 1 - 2 years | 1 | United States | 06-CALIFORNIA | 1 | White Collar | 03 | 03xx-GENERAL ADMIN, CLERICAL, & OFFICE SVCS | 0301-MISCELLANEOUS ADMINISTRATION AND PROGRAM | Administrative | 1 | General Schedule and Equivalently Graded (GSEG... | 11 | Standard GSEG Pay Plans | GS | GS-GENERAL SCHEDULE | $90,000 - $99,999 | 1 | Permanent | 15-Competitive Service - Career-Conditional | 1 | Full-time | Full-time Nonseasonal | 721.0 | 1853 | 101641.124025 | -8668.124025 |
| AGYSUB | SEP | DATECODE | AGELVL | GENDER | GSEGRD | LOSLVL | LOC | OCC | PATCO | PPGRD | SALLVL | TOA | WORKSCH | COUNT | SALARY | LOS | AGYTYP | AGYTYPT | AGY | AGYT | AGYSUBT | QTR | AGELVLT | LOSLVLT | LOCTYP | LOCTYPT | LOCT | OCCTYP | OCCTYPT | OCCFAM | OCCFAMT | OCCT | PATCOT | PPTYP | PPTYPT | PPGROUP | PPGROUPT | PAYPLAN | PAYPLANT | SALLVLT | TOATYP | TOATYPT | TOAT | WSTYP | WSTYPT | WORKSCHT | SEPCount_EFDATE_OCC | SEPCount_EFDATE_LOC | IndAvgSalary | SalaryOverUnderIndAvg | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 8223188 | ZU00 | NS | 201509 | D | NaN | NaN | C | 11 | 0301 | 2 | AD-00 | G | 48 | F | NaN | 76377.0 | 4.8 | 4 | Small Independent Agencies (less than 100 empl... | ZU | ZU-DWIGHT D. EISENHOWER MEMORIAL COMMISSION | ZU00-DWIGHT D. EISENHOWER MEMORIAL COMMISSION | 4 | 30-34 | 3 - 4 years | 1 | United States | 11-DISTRICT OF COLUMBIA | 1 | White Collar | 03 | 03xx-GENERAL ADMIN, CLERICAL, & OFFICE SVCS | 0301-MISCELLANEOUS ADMINISTRATION AND PROGRAM | Administrative | 3 | Other White Collar Pay Plans | 31 | Governmentwide or Multi-Agency Plans | AD | AD-ADMINISTRATIVELY DETERMINED RATES, NOT ELSE... | $70,000 - $79,999 | 2 | Non-permanent | 48-Excepted Service - Other | 1 | Full-time | Full-time Nonseasonal | 721.0 | 1391 | 115840.182250 | -39463.182250 |
| 8223189 | ZU00 | NS | 201509 | K | NaN | NaN | D | 11 | 0301 | 2 | AD-00 | M | 48 | F | NaN | 139517.0 | 7.0 | 4 | Small Independent Agencies (less than 100 empl... | ZU | ZU-DWIGHT D. EISENHOWER MEMORIAL COMMISSION | ZU00-DWIGHT D. EISENHOWER MEMORIAL COMMISSION | 4 | 65 or more | 5 - 9 years | 1 | United States | 11-DISTRICT OF COLUMBIA | 1 | White Collar | 03 | 03xx-GENERAL ADMIN, CLERICAL, & OFFICE SVCS | 0301-MISCELLANEOUS ADMINISTRATION AND PROGRAM | Administrative | 3 | Other White Collar Pay Plans | 31 | Governmentwide or Multi-Agency Plans | AD | AD-ADMINISTRATIVELY DETERMINED RATES, NOT ELSE... | $130,000 - $139,999 | 2 | Non-permanent | 48-Excepted Service - Other | 1 | Full-time | Full-time Nonseasonal | 721.0 | 1391 | 115840.182250 | 23676.817750 |
| 8223190 | ZU00 | NS | 201509 | K | NaN | NaN | D | 11 | 0301 | 2 | AD-00 | O | 48 | F | NaN | 158671.0 | 7.0 | 4 | Small Independent Agencies (less than 100 empl... | ZU | ZU-DWIGHT D. EISENHOWER MEMORIAL COMMISSION | ZU00-DWIGHT D. EISENHOWER MEMORIAL COMMISSION | 4 | 65 or more | 5 - 9 years | 1 | United States | 11-DISTRICT OF COLUMBIA | 1 | White Collar | 03 | 03xx-GENERAL ADMIN, CLERICAL, & OFFICE SVCS | 0301-MISCELLANEOUS ADMINISTRATION AND PROGRAM | Administrative | 3 | Other White Collar Pay Plans | 31 | Governmentwide or Multi-Agency Plans | AD | AD-ADMINISTRATIVELY DETERMINED RATES, NOT ELSE... | $150,000 - $159,999 | 2 | Non-permanent | 48-Excepted Service - Other | 1 | Full-time | Full-time Nonseasonal | 721.0 | 1391 | 115840.182250 | 42830.817750 |
| 8223191 | ZU00 | NS | 201509 | B | NaN | NaN | B | 11 | 0301 | 2 | AD-00 | C | 48 | F | NaN | 36244.0 | 1.6 | 4 | Small Independent Agencies (less than 100 empl... | ZU | ZU-DWIGHT D. EISENHOWER MEMORIAL COMMISSION | ZU00-DWIGHT D. EISENHOWER MEMORIAL COMMISSION | 4 | 20-24 | 1 - 2 years | 1 | United States | 11-DISTRICT OF COLUMBIA | 1 | White Collar | 03 | 03xx-GENERAL ADMIN, CLERICAL, & OFFICE SVCS | 0301-MISCELLANEOUS ADMINISTRATION AND PROGRAM | Administrative | 3 | Other White Collar Pay Plans | 31 | Governmentwide or Multi-Agency Plans | AD | AD-ADMINISTRATIVELY DETERMINED RATES, NOT ELSE... | $30,000 - $39,999 | 2 | Non-permanent | 48-Excepted Service - Other | 1 | Full-time | Full-time Nonseasonal | 721.0 | 1391 | 115840.182250 | -79596.182250 |
| 8223192 | ZU00 | NS | 201509 | E | NaN | NaN | D | 11 | 0505 | 2 | AD-00 | I | 48 | F | NaN | 99288.0 | 5.0 | 4 | Small Independent Agencies (less than 100 empl... | ZU | ZU-DWIGHT D. EISENHOWER MEMORIAL COMMISSION | ZU00-DWIGHT D. EISENHOWER MEMORIAL COMMISSION | 4 | 35-39 | 5 - 9 years | 1 | United States | 11-DISTRICT OF COLUMBIA | 1 | White Collar | 05 | 05xx-ACCOUNTING AND BUDGET | 0505-FINANCIAL MANAGEMENT | Administrative | 3 | Other White Collar Pay Plans | 31 | Governmentwide or Multi-Agency Plans | AD | AD-ADMINISTRATIVELY DETERMINED RATES, NOT ELSE... | $90,000 - $99,999 | 2 | Non-permanent | 48-Excepted Service - Other | 1 | Full-time | Full-time Nonseasonal | 7.0 | 1391 | 148382.833333 | -49094.833333 |
print(len(OPMDataMerged[OPMDataMerged["SEPCount_EFDATE_OCC"].isnull()]))
display(OPMDataMerged[OPMDataMerged["SEPCount_EFDATE_OCC"].isnull()][["SEP","DATECODE", "OCC"]].drop_duplicates())
50993
| SEP | DATECODE | OCC | |
|---|---|---|---|
| 217479 | NS | 201412 | 7402 |
| 217582 | NS | 201412 | 7420 |
| 217603 | NS | 201412 | 1051 |
| 217663 | NS | 201412 | 1054 |
| 218685 | NS | 201412 | 2504 |
| 218871 | NS | 201412 | 8201 |
| 218999 | NS | 201412 | 4104 |
| 219003 | NS | 201412 | 4715 |
| 219135 | NS | 201412 | 0698 |
| 220085 | NS | 201412 | 0019 |
| 220426 | NS | 201412 | 3602 |
| 221497 | NS | 201412 | 2608 |
| 221637 | NS | 201412 | 3725 |
| 224242 | NS | 201412 | 6968 |
| 225410 | NS | 201412 | 0392 |
| 226132 | NS | 201412 | 3606 |
| 228440 | NS | 201412 | 2601 |
| 231003 | NS | 201412 | 3940 |
| 231189 | NS | 201412 | 5439 |
| 246316 | NS | 201412 | 1725 |
| 246379 | NS | 201412 | 5317 |
| 246874 | NS | 201412 | 5737 |
| 247551 | NS | 201412 | 1386 |
| 254687 | NS | 201412 | 0394 |
| 259606 | NS | 201412 | 4819 |
| 264047 | NS | 201412 | 2144 |
| 266228 | NS | 201412 | 1056 |
| 268830 | NS | 201412 | 5736 |
| 270371 | NS | 201412 | 0021 |
| 271810 | NS | 201412 | 3872 |
| 271986 | NS | 201412 | 4301 |
| 273244 | NS | 201412 | 3701 |
| 273326 | NS | 201412 | 6656 |
| 273665 | NS | 201412 | 8601 |
| 275118 | NS | 201412 | 3858 |
| 275185 | NS | 201412 | 4745 |
| 277206 | NS | 201412 | 4816 |
| 279195 | NS | 201412 | 1699 |
| 284633 | NS | 201412 | 5423 |
| 289472 | NS | 201412 | 1321 |
| 295232 | NS | 201412 | 3727 |
| 305466 | NS | 201412 | 1521 |
| 319867 | NS | 201412 | 0642 |
| 325062 | NS | 201412 | 4373 |
| 332634 | NS | 201412 | 2110 |
| 349140 | NS | 201412 | 0134 |
| 376747 | NS | 201412 | 0435 |
| 377161 | NS | 201412 | 1382 |
| 380400 | NS | 201412 | 0440 |
| 380444 | NS | 201412 | 0890 |
| 380485 | NS | 201412 | 1221 |
| 380534 | NS | 201412 | 0799 |
| 380549 | NS | 201412 | 0471 |
| 381610 | NS | 201412 | 5002 |
| 381660 | NS | 201412 | 0302 |
| 382823 | NS | 201412 | 4737 |
| 383757 | NS | 201412 | 1384 |
| 387315 | NS | 201412 | 3511 |
| 395468 | NS | 201412 | 1380 |
| 407172 | NS | 201412 | 0880 |
| 417564 | NS | 201412 | 1202 |
| 422349 | NS | 201412 | 0184 |
| 431266 | NS | 201412 | 5729 |
| 444970 | NS | 201412 | 3515 |
| 455591 | NS | 201412 | 4414 |
| 456269 | NS | 201412 | 1850 |
| 457198 | NS | 201412 | 0160 |
| 461672 | NS | 201412 | 0136 |
| 475474 | NS | 201412 | 1374 |
| 475784 | NS | 201412 | 6517 |
| 475903 | NS | 201412 | 6605 |
| 480003 | NS | 201412 | 5310 |
| 485323 | NS | 201412 | 3605 |
| 500481 | NS | 201412 | 4741 |
| 503007 | NS | 201412 | 1397 |
| 505340 | NS | 201412 | 3314 |
| 506488 | NS | 201412 | 5323 |
| 516998 | NS | 201412 | 4101 |
| 519478 | NS | 201412 | 0322 |
| 558933 | NS | 201412 | 4010 |
| 559676 | NS | 201412 | 0648 |
| 593579 | NS | 201412 | 3301 |
| 596650 | NS | 201412 | 3101 |
| 625941 | NS | 201412 | 7603 |
| 661444 | NS | 201412 | 4807 |
| 661550 | NS | 201412 | 3428 |
| 662101 | NS | 201412 | 5738 |
| 676144 | NS | 201412 | 5205 |
| 685955 | NS | 201412 | 6505 |
| 686300 | NS | 201412 | 3546 |
| 686445 | NS | 201412 | 5427 |
| 704369 | NS | 201412 | 2161 |
| 709277 | NS | 201412 | 9927 |
| 709522 | NS | 201412 | 9968 |
| 711524 | NS | 201412 | 9944 |
| 711608 | NS | 201412 | 9916 |
| 711636 | NS | 201412 | 9957 |
| 712047 | NS | 201412 | 9960 |
| 712126 | NS | 201412 | 9971 |
| 722081 | NS | 201412 | 1226 |
| 722670 | NS | 201412 | 1223 |
| 725221 | NS | 201412 | 1299 |
| 766570 | NS | 201412 | 1999 |
| 859013 | NS | 201412 | 1541 |
| 966674 | NS | 201412 | 0106 |
| 966676 | NS | 201412 | 0243 |
| 966716 | NS | 201412 | 0140 |
| 971520 | NS | 201412 | 0357 |
| 1087460 | NS | 201412 | 1046 |
| 1145227 | NS | 201412 | 4717 |
| 1345880 | NS | 201412 | 2501 |
| 1359410 | NS | 201412 | 3910 |
| 1363515 | NS | 201412 | 9961 |
| 1384266 | NS | 201412 | 9905 |
| 1389961 | NS | 201412 | 5419 |
| 1502113 | NS | 201412 | 3604 |
| 1503521 | NS | 201412 | 3808 |
| 1528597 | NS | 201412 | 1021 |
| 1534692 | NS | 201412 | 9942 |
| 1534710 | NS | 201412 | 9997 |
| 1534741 | NS | 201412 | 9975 |
| 1534742 | NS | 201412 | 9930 |
| 1534756 | NS | 201412 | 9945 |
| 1534808 | NS | 201412 | 9972 |
| 1534811 | NS | 201412 | 9995 |
| 1534822 | NS | 201412 | 9940 |
| 1534917 | NS | 201412 | 9993 |
| 1534931 | NS | 201412 | 9982 |
| 1535050 | NS | 201412 | 9999 |
| 1535060 | NS | 201412 | 9915 |
| 1535179 | NS | 201412 | 9955 |
| 1535640 | NS | 201412 | 9919 |
| 1535643 | NS | 201412 | 9918 |
| 1536227 | NS | 201412 | 9914 |
| 1537487 | NS | 201412 | 9921 |
| 1538507 | NS | 201412 | 9903 |
| 1562285 | NS | 201412 | 5221 |
| 1620441 | NS | 201412 | 1831 |
| 1846895 | NS | 201412 | 5440 |
| 1846946 | NS | 201412 | 3513 |
| 1848770 | NS | 201412 | 4406 |
| 1848778 | NS | 201412 | 4454 |
| 1872827 | NS | 201412 | 0593 |
| 1906935 | NS | 201412 | 0625 |
| 1937597 | NS | 201412 | 0637 |
| 2209093 | NS | 201503 | 1054 |
| 2209135 | NS | 201503 | 0050 |
| 2209165 | NS | 201503 | 7420 |
| 2209549 | NS | 201503 | 4805 |
| 2209562 | NS | 201503 | 7401 |
| 2209567 | NS | 201503 | 0062 |
| 2209830 | NS | 201503 | 1051 |
| 2210138 | NS | 201503 | 5767 |
| 2210218 | NS | 201503 | 2504 |
| 2210251 | NS | 201503 | 3940 |
| 2210298 | NS | 201503 | 4715 |
| 2210777 | NS | 201503 | 0017 |
| 2210790 | NS | 201503 | 0019 |
| 2210923 | NS | 201503 | 5026 |
| 2211013 | NS | 201503 | 3602 |
| 2211337 | NS | 201503 | 4255 |
| 2211397 | NS | 201503 | 3606 |
| 2211430 | NS | 201503 | 3809 |
| 2211601 | NS | 201503 | 1501 |
| 2211622 | NS | 201503 | 2608 |
| 2211840 | NS | 201503 | 3901 |
| 2211850 | NS | 201503 | 0698 |
| 2212525 | NS | 201503 | 0667 |
| 2213232 | NS | 201503 | 8610 |
| 2216698 | NS | 201503 | 4605 |
| 2216720 | NS | 201503 | 5439 |
| 2217256 | NS | 201503 | 1015 |
| 2221243 | NS | 201503 | 3725 |
| 2237081 | NS | 201503 | 5737 |
| 2237389 | NS | 201503 | 5317 |
| 2237807 | NS | 201503 | 1725 |
| 2237838 | NS | 201503 | 0131 |
| 2238836 | NS | 201503 | 1386 |
| 2239148 | NS | 201503 | 4417 |
| 2239413 | NS | 201503 | 4401 |
| 2245721 | NS | 201503 | 5876 |
| 2247270 | NS | 201503 | 4201 |
| 2250530 | NS | 201503 | 2135 |
| 2251137 | NS | 201503 | 0394 |
| 2254034 | NS | 201503 | 4819 |
| 2255121 | NS | 201503 | 7001 |
| 2255147 | NS | 201503 | 0021 |
| 2255494 | NS | 201503 | 2144 |
| 2257059 | NS | 201503 | 4602 |
| 2257560 | NS | 201503 | 4601 |
| 2258388 | NS | 201503 | 1056 |
| 2262895 | NS | 201503 | 3769 |
| 2262896 | NS | 201503 | 3707 |
| 2262911 | NS | 201503 | 4850 |
| 2263867 | NS | 201503 | 6656 |
| 2264417 | NS | 201503 | 4745 |
| 2264609 | NS | 201503 | 3872 |
| 2264927 | NS | 201503 | 4616 |
| 2265294 | NS | 201503 | 4301 |
| 2266492 | NS | 201503 | 8601 |
| 2267324 | NS | 201503 | 3727 |
| 2268045 | NS | 201503 | 7006 |
| 2269170 | NS | 201503 | 3712 |
| 2269386 | NS | 201503 | 2032 |
| 2273426 | NS | 201503 | 1521 |
| 2273438 | NS | 201503 | 0688 |
| 2274894 | NS | 201503 | 3858 |
| 2278199 | NS | 201503 | 4373 |
| 2282112 | NS | 201503 | 3401 |
| 2290158 | NS | 201503 | 4816 |
| 2305124 | NS | 201503 | 1321 |
| 2312141 | NS | 201503 | 5313 |
| 2319977 | NS | 201503 | 0642 |
| 2322021 | NS | 201503 | 1372 |
| 2326923 | NS | 201503 | 2110 |
| 2332047 | NS | 201503 | 1815 |
| 2367236 | NS | 201503 | 1146 |
| 2367963 | NS | 201503 | 1382 |
| 2368576 | NS | 201503 | 0435 |
| 2371118 | NS | 201503 | 0471 |
| 2371219 | NS | 201503 | 0487 |
| 2371445 | NS | 201503 | 1221 |
| 2373151 | NS | 201503 | 5002 |
| 2373803 | NS | 201503 | 0799 |
| 2373812 | NS | 201503 | 1384 |
| 2373939 | NS | 201503 | 0302 |
| 2375066 | NS | 201503 | 5001 |
| 2382890 | NS | 201503 | 0135 |
| 2383750 | NS | 201503 | 1380 |
| 2394962 | NS | 201503 | 5786 |
| 2398815 | NS | 201503 | 1202 |
| 2421724 | NS | 201503 | 5729 |
| 2422326 | NS | 201503 | 0309 |
| 2429714 | NS | 201503 | 3511 |
| 2435949 | NS | 201503 | 3515 |
| 2445822 | NS | 201503 | 4414 |
| 2445959 | NS | 201503 | 4402 |
| 2446076 | NS | 201503 | 1850 |
| 2446395 | NS | 201503 | 0160 |
| 2451711 | NS | 201503 | 0136 |
| 2465677 | NS | 201503 | 6517 |
| 2465911 | NS | 201503 | 1374 |
| 2469001 | NS | 201503 | 7601 |
| 2469608 | NS | 201503 | 5310 |
| 2473512 | NS | 201503 | 3605 |
| 2478500 | NS | 201503 | 4741 |
| 2481863 | NS | 201503 | 1630 |
| 2487747 | NS | 201503 | 5042 |
| 2492992 | NS | 201503 | 1397 |
| 2493609 | NS | 201503 | 5318 |
| ... | ... | ... | ... |
| 4493254 | NS | 201506 | 3605 |
| 4500627 | NS | 201506 | 5784 |
| 4501690 | NS | 201506 | 5323 |
| 4505453 | NS | 201506 | 3314 |
| 4511881 | NS | 201506 | 0322 |
| 4567936 | NS | 201506 | 0313 |
| 4590882 | NS | 201506 | 4754 |
| 4591246 | NS | 201506 | 3101 |
| 4618223 | NS | 201506 | 3301 |
| 4625645 | NS | 201506 | 7603 |
| 4647430 | NS | 201506 | 3106 |
| 4648437 | NS | 201506 | 4101 |
| 4656879 | NS | 201506 | 0873 |
| 4659315 | NS | 201506 | 5738 |
| 4660107 | NS | 201506 | 3802 |
| 4666481 | NS | 201506 | 4807 |
| 4673196 | NS | 201506 | 5205 |
| 4683095 | NS | 201506 | 6505 |
| 4683209 | NS | 201506 | 3546 |
| 4683500 | NS | 201506 | 5427 |
| 4706313 | NS | 201506 | 9924 |
| 4706406 | NS | 201506 | 9954 |
| 4706448 | NS | 201506 | 9923 |
| 4706828 | NS | 201506 | 9932 |
| 4706944 | NS | 201506 | 9920 |
| 4706992 | NS | 201506 | 9916 |
| 4707380 | NS | 201506 | 9971 |
| 4707627 | NS | 201506 | 9960 |
| 4711283 | NS | 201506 | 9944 |
| 4718825 | NS | 201506 | 1226 |
| 4719354 | NS | 201506 | 1223 |
| 4720834 | NS | 201506 | 1299 |
| 4838452 | NS | 201506 | 1163 |
| 4941776 | NS | 201506 | 0082 |
| 4967369 | NS | 201506 | 0140 |
| 4967373 | NS | 201506 | 0243 |
| 4967385 | NS | 201506 | 0106 |
| 4971515 | NS | 201506 | 0357 |
| 5079100 | NS | 201506 | 1046 |
| 5238592 | NS | 201506 | 1889 |
| 5364837 | NS | 201506 | 9905 |
| 5366360 | NS | 201506 | 3910 |
| 5379475 | NS | 201506 | 9961 |
| 5397362 | NS | 201506 | 4416 |
| 5397878 | NS | 201506 | 5419 |
| 5515876 | NS | 201506 | 3808 |
| 5525861 | NS | 201506 | 1021 |
| 5543637 | NS | 201506 | 9982 |
| 5543654 | NS | 201506 | 9942 |
| 5543662 | NS | 201506 | 9997 |
| 5543680 | NS | 201506 | 9955 |
| 5543695 | NS | 201506 | 9975 |
| 5543698 | NS | 201506 | 9906 |
| 5543700 | NS | 201506 | 9991 |
| 5543706 | NS | 201506 | 9908 |
| 5543725 | NS | 201506 | 9930 |
| 5543741 | NS | 201506 | 9976 |
| 5543817 | NS | 201506 | 9919 |
| 5543902 | NS | 201506 | 9999 |
| 5543929 | NS | 201506 | 9929 |
| 5544204 | NS | 201506 | 9940 |
| 5544280 | NS | 201506 | 9939 |
| 5544281 | NS | 201506 | 9914 |
| 5544294 | NS | 201506 | 9918 |
| 5544388 | NS | 201506 | 9915 |
| 5545181 | NS | 201506 | 9921 |
| 5560038 | NS | 201506 | 9904 |
| 5570248 | NS | 201506 | 5221 |
| 5612184 | NS | 201506 | 3428 |
| 5631455 | NS | 201506 | 1831 |
| 5708205 | NS | 201506 | 2125 |
| 5846894 | NS | 201506 | 5440 |
| 5847347 | NS | 201506 | 3513 |
| 5848754 | NS | 201506 | 4406 |
| 5848756 | NS | 201506 | 4441 |
| 5848778 | NS | 201506 | 4454 |
| 5848780 | NS | 201506 | 4449 |
| 5874147 | NS | 201506 | 0593 |
| 5899552 | NS | 201506 | 0625 |
| 5915763 | NS | 201506 | 0637 |
| 6215137 | NS | 201509 | 7420 |
| 6215177 | NS | 201509 | 1054 |
| 6215183 | NS | 201509 | 0062 |
| 6215311 | NS | 201509 | 1051 |
| 6216048 | NS | 201509 | 0319 |
| 6216143 | NS | 201509 | 0332 |
| 6216452 | NS | 201509 | 5026 |
| 6216656 | NS | 201509 | 3602 |
| 6216697 | NS | 201509 | 4255 |
| 6216714 | NS | 201509 | 3610 |
| 6216843 | NS | 201509 | 4715 |
| 6217038 | NS | 201509 | 8610 |
| 6217069 | NS | 201509 | 1501 |
| 6217109 | NS | 201509 | 4714 |
| 6217177 | NS | 201509 | 8201 |
| 6218219 | NS | 201509 | 0017 |
| 6221598 | NS | 201509 | 5439 |
| 6221611 | NS | 201509 | 6511 |
| 6222813 | NS | 201509 | 3901 |
| 6223238 | NS | 201509 | 6968 |
| 6224360 | NS | 201509 | 3725 |
| 6242835 | NS | 201509 | 5317 |
| 6242837 | NS | 201509 | 7305 |
| 6243763 | NS | 201509 | 3111 |
| 6244182 | NS | 201509 | 1725 |
| 6244772 | NS | 201509 | 3606 |
| 6245272 | NS | 201509 | 4401 |
| 6245800 | NS | 201509 | 1386 |
| 6246495 | NS | 201509 | 1815 |
| 6248929 | NS | 201509 | 1361 |
| 6251962 | NS | 201509 | 4201 |
| 6252800 | NS | 201509 | 5401 |
| 6253625 | NS | 201509 | 5737 |
| 6260660 | NS | 201509 | 4819 |
| 6261977 | NS | 201509 | 7001 |
| 6262910 | NS | 201509 | 2144 |
| 6265927 | NS | 201509 | 0021 |
| 6266002 | NS | 201509 | 4602 |
| 6269692 | NS | 201509 | 0967 |
| 6269891 | NS | 201509 | 4840 |
| 6270010 | NS | 201509 | 3707 |
| 6270144 | NS | 201509 | 5423 |
| 6270200 | NS | 201509 | 6656 |
| 6270425 | NS | 201509 | 3872 |
| 6270432 | NS | 201509 | 4745 |
| 6270882 | NS | 201509 | 4361 |
| 6271499 | NS | 201509 | 3701 |
| 6271656 | NS | 201509 | 3727 |
| 6272327 | NS | 201509 | 4816 |
| 6272630 | NS | 201509 | 4850 |
| 6273748 | NS | 201509 | 3858 |
| 6274513 | NS | 201509 | 1222 |
| 6276289 | NS | 201509 | 4616 |
| 6276382 | NS | 201509 | 4301 |
| 6277379 | NS | 201509 | 7006 |
| 6280721 | NS | 201509 | 8601 |
| 6308684 | NS | 201509 | 1321 |
| 6325366 | NS | 201509 | 1056 |
| 6327220 | NS | 201509 | 1521 |
| 6334577 | NS | 201509 | 2110 |
| 6346443 | NS | 201509 | 4417 |
| 6377693 | NS | 201509 | 0434 |
| 6378110 | NS | 201509 | 1999 |
| 6378827 | NS | 201509 | 0440 |
| 6378853 | NS | 201509 | 0487 |
| 6378954 | NS | 201509 | 0437 |
| 6379113 | NS | 201509 | 5002 |
| 6379245 | NS | 201509 | 1384 |
| 6380466 | NS | 201509 | 0410 |
| 6381133 | NS | 201509 | 0308 |
| 6381165 | NS | 201509 | 0302 |
| 6391135 | NS | 201509 | 0135 |
| 6392086 | NS | 201509 | 1380 |
| 6392140 | NS | 201509 | 0965 |
| 6405195 | NS | 201509 | 0880 |
| 6428400 | NS | 201509 | 1202 |
| 6433233 | NS | 201509 | 5729 |
| 6435611 | NS | 201509 | 0309 |
| 6438731 | NS | 201509 | 0184 |
| 6453143 | NS | 201509 | 3515 |
| 6464352 | NS | 201509 | 4414 |
| 6464503 | NS | 201509 | 1850 |
| 6470218 | NS | 201509 | 0136 |
| 6483824 | NS | 201509 | 1374 |
| 6485608 | NS | 201509 | 2501 |
| 6488173 | NS | 201509 | 1630 |
| 6490604 | NS | 201509 | 5310 |
| 6490614 | NS | 201509 | 4741 |
| 6509539 | NS | 201509 | 3605 |
| 6511085 | NS | 201509 | 0072 |
| 6511515 | NS | 201509 | 1397 |
| 6512110 | NS | 201509 | 5782 |
| 6512216 | NS | 201509 | 5323 |
| 6516672 | NS | 201509 | 3314 |
| 6584508 | NS | 201509 | 0635 |
| 6593181 | NS | 201509 | 0313 |
| 6606313 | NS | 201509 | 3101 |
| 6628632 | NS | 201509 | 3301 |
| 6639321 | NS | 201509 | 7603 |
| 6651971 | NS | 201509 | 1046 |
| 6658080 | NS | 201509 | 3106 |
| 6658384 | NS | 201509 | 4101 |
| 6666886 | NS | 201509 | 0873 |
| 6667185 | NS | 201509 | 4373 |
| 6669581 | NS | 201509 | 4807 |
| 6670232 | NS | 201509 | 5738 |
| 6672326 | NS | 201509 | 3802 |
| 6693020 | NS | 201509 | 5427 |
| 6711618 | NS | 201509 | 2161 |
| 6713304 | NS | 201509 | 0958 |
| 6716480 | NS | 201509 | 9973 |
| 6716659 | NS | 201509 | 9916 |
| 6716801 | NS | 201509 | 9932 |
| 6717186 | NS | 201509 | 9971 |
| 6718325 | NS | 201509 | 9965 |
| 6719926 | NS | 201509 | 9960 |
| 6729094 | NS | 201509 | 1226 |
| 6729105 | NS | 201509 | 1223 |
| 6731464 | NS | 201509 | 1299 |
| 6741881 | NS | 201509 | 5313 |
| 6803145 | NS | 201509 | 1730 |
| 6820061 | NS | 201509 | 6941 |
| 6847199 | NS | 201509 | 1163 |
| 6868697 | NS | 201509 | 1541 |
| 6976225 | NS | 201509 | 0140 |
| 6976233 | NS | 201509 | 0106 |
| 6976245 | NS | 201509 | 0243 |
| 6980096 | NS | 201509 | 0357 |
| 7155776 | NS | 201509 | 4717 |
| 7247692 | NS | 201509 | 1881 |
| 7371930 | NS | 201509 | 3910 |
| 7376502 | NS | 201509 | 9961 |
| 7392449 | NS | 201509 | 0485 |
| 7404982 | NS | 201509 | 4403 |
| 7405044 | NS | 201509 | 4416 |
| 7405049 | NS | 201509 | 5419 |
| 7521217 | NS | 201509 | 3808 |
| 7524148 | NS | 201509 | 3604 |
| 7536239 | NS | 201509 | 1021 |
| 7552743 | NS | 201509 | 9991 |
| 7552774 | NS | 201509 | 9975 |
| 7552775 | NS | 201509 | 9998 |
| 7552813 | NS | 201509 | 9976 |
| 7552825 | NS | 201509 | 9994 |
| 7552870 | NS | 201509 | 9988 |
| 7552961 | NS | 201509 | 9940 |
| 7552962 | NS | 201509 | 9982 |
| 7553012 | NS | 201509 | 9915 |
| 7553021 | NS | 201509 | 9930 |
| 7553042 | NS | 201509 | 9929 |
| 7553061 | NS | 201509 | 9999 |
| 7553126 | NS | 201509 | 9993 |
| 7553127 | NS | 201509 | 9908 |
| 7553159 | NS | 201509 | 9955 |
| 7553740 | NS | 201509 | 9914 |
| 7553846 | NS | 201509 | 9939 |
| 7554073 | NS | 201509 | 9921 |
| 7554679 | NS | 201509 | 9919 |
| 7554855 | NS | 201509 | 9918 |
| 7569222 | NS | 201509 | 9904 |
| 7579652 | NS | 201509 | 5221 |
| 7612169 | NS | 201509 | 3428 |
| 7717999 | NS | 201509 | 2125 |
| 7851216 | NS | 201509 | 5440 |
| 7851838 | NS | 201509 | 3513 |
| 7853201 | NS | 201509 | 4406 |
| 7853227 | NS | 201509 | 4454 |
| 7884942 | NS | 201509 | 0593 |
| 7965633 | NS | 201509 | 0625 |
| 8001140 | NS | 201509 | 0637 |
660 rows × 3 columns
These 50993 Non-Separation observations do not have coverage within the Separation Dataset, thus, we will remove these observations as out of scope demographic in our analysis. Any attempt in predicting these values will not have enough data to support a significant response.
OPMDataMerged = OPMDataMerged[OPMDataMerged["SEPCount_EFDATE_OCC"].notnull()]
print(len(OPMDataMerged[OPMDataMerged["SEPCount_EFDATE_OCC"].isnull()]))
print(len(OPMDataMerged))
0 8172200
print(len(OPMDataMerged[OPMDataMerged["SEPCount_EFDATE_LOC"].isnull()]))
display(OPMDataMerged[OPMDataMerged["SEPCount_EFDATE_LOC"].isnull()][["SEP","DATECODE","LOC"]].drop_duplicates())
0
| SEP | DATECODE | LOC |
|---|
print(len(OPMDataMerged[OPMDataMerged["IndAvgSalary"].isnull()]))
display(OPMDataMerged[OPMDataMerged["IndAvgSalary"].isnull()][["QTR", "SEP","OCCT", "PPGRD", "WORKSCHT"]].drop_duplicates())
1293
| QTR | SEP | OCCT | PPGRD | WORKSCHT | |
|---|---|---|---|---|---|
| 257 | 4 | SC | 7401-MISC FOOD PREPARATION AND SERVING | WG-01 | Full-time Nonseasonal |
| 627 | 4 | SC | 1301-GENERAL PHYSICAL SCIENCE | AD-24 | Part-time Nonseasonal |
| 697 | 4 | SJ | 0199-SOCIAL SCIENCE STUDENT TRAINEE | GS-02 | Intermittent Nonseasonal |
| 749 | 4 | SC | 3940-BROADCASTING EQUIPMENT OPERATING | WG-10 | Full-time Nonseasonal |
| 2401 | 4 | SJ | 0399-ADMINISTRATION AND OFFICE SUPPORT STUDENT... | GS-02 | Intermittent Seasonal |
| 3412 | 2 | SC | 5003-GARDENING | WG-04 | Full-time Seasonal |
| 3471 | 1 | SA | 5003-GARDENING | WG-04 | Full-time Seasonal |
| 3551 | 3 | SD | 5716-ENGINEERING EQUIPMENT OPERATING | WS-14 | Full-time Nonseasonal |
| 4937 | 3 | SC | 0819-ENVIRONMENTAL ENGINEERING | GS-11 | Part-time Job Sharer Nonseasonal |
| 5285 | 1 | SD | 5716-ENGINEERING EQUIPMENT OPERATING | WG-08 | Intermittent Seasonal |
| 5363 | 4 | SJ | 0189-RECREATION AID AND ASSISTANT | GS-03 | Intermittent Nonseasonal |
| 5763 | 1 | SD | 2005-SUPPLY CLERICAL AND TECHNICIAN | GS-04 | Part-time Job Sharer Nonseasonal |
| 6079 | 3 | SC | 0180-PSYCHOLOGY | NH-02 | Full-time Nonseasonal |
| 6957 | 3 | SD | 0810-CIVIL ENGINEERING | DR-03 | Full-time Nonseasonal |
| 7015 | 1 | SA | 1306-HEALTH PHYSICS | DR-01 | Full-time Nonseasonal |
| 7376 | 4 | SC | 1699-EQUIPMENT AND FACILITIES MANAGEMENT STUDE... | GS-05 | Full-time Nonseasonal |
| 7395 | 3 | SC | 0599-FINANCIAL MANAGEMENT STUDENT TRAINEE | DU-01 | Full-time Nonseasonal |
| 7464 | 3 | SD | 3769-SHOT PEENING MACHINE OPERATING | WS-07 | Full-time Nonseasonal |
| 7512 | 4 | SC | 0840-NUCLEAR ENGINEERING | DR-03 | Full-time Nonseasonal |
| 7675 | 2 | SD | 4714-MODEL MAKING | WL-15 | Full-time Nonseasonal |
| 7727 | 4 | SC | 0189-RECREATION AID AND ASSISTANT | GS-02 | Part-time Seasonal |
| 7877 | 4 | SJ | 0189-RECREATION AID AND ASSISTANT | GS-02 | Part-time Seasonal |
| 8054 | 4 | SC | 0665-SPEECH PATHOLOGY AND AUDIOLOGY | DR-03 | Full-time Nonseasonal |
| 8160 | 4 | SC | 4102-PAINTING | WG-05 | Part-time Seasonal |
| 8216 | 2 | SA | 5725-CRANE OPERATING | WG-08 | Full-time Nonseasonal |
| 8320 | 4 | SD | 5401-MISC INDUSTRIAL EQUIPMENT OPERATION | WS-11 | Full-time Nonseasonal |
| 8325 | 1 | SC | 0189-RECREATION AID AND ASSISTANT | DU-01 | Part-time Nonseasonal |
| 8389 | 4 | SD | 3705-NON-DESTRUCTIVE TESTING | WS-16 | Full-time Nonseasonal |
| 8435 | 4 | SD | 1330-ASTRONOMY AND SPACE SCIENCE | DR-04 | Full-time Nonseasonal |
| 8449 | 1 | SK | 4102-PAINTING | WG-05 | Part-time Seasonal |
| 9741 | 4 | SC | 0189-RECREATION AID AND ASSISTANT | GS-03 | Part-time Seasonal |
| 9890 | 1 | SI | 8801-MISCELLANEOUS AIRCRAFT OVERHAUL | WG-08 | Part-time Nonseasonal |
| 9903 | 1 | SD | 0610-NURSE | DR-01 | Full-time Nonseasonal |
| 9916 | 2 | SD | 0130-FOREIGN AFFAIRS | DO-02 | Full-time Nonseasonal |
| 10139 | 4 | SC | 6901-MISC WAREHOUSING AND STOCK HANDLING | WG-06 | Part-time Job Sharer Nonseasonal |
| 10656 | 2 | SC | 5309-HEATING & BOILER PLANT EQUIPMT MECHANIC | WL-11 | Full-time Nonseasonal |
| 11491 | 4 | SC | 1008-INTERIOR DESIGN | GG-12 | Full-time Nonseasonal |
| 11516 | 4 | SI | 0854-COMPUTER ENGINEERING | GG-11 | Full-time Nonseasonal |
| 11941 | 1 | SJ | 0201-HUMAN RESOURCES MANAGEMENT | GS-06 | Full-time Nonseasonal |
| 12265 | 4 | SJ | 0335-COMPUTER CLERK AND ASSISTANT | GS-09 | Part-time Nonseasonal |
| 12541 | 2 | SJ | 6501-MISC AMMUN, EXPLOSIVES, & TOXIC MATER WORK | WG-12 | Full-time Nonseasonal |
| 13028 | 3 | SJ | 5378-POWERED SUPPORT SYSTEMS MECHANIC | WG-10 | Part-time Nonseasonal |
| 13029 | 2 | SJ | 2610-ELECTRONIC INTEGRATED SYSTEMS MECHANIC | WG-12 | Part-time Nonseasonal |
| 13643 | 3 | SJ | 8602-AIRCRAFT ENGINE MECHANIC | WG-04 | Full-time Nonseasonal |
| 14084 | 3 | SJ | 0340-PROGRAM MANAGEMENT | GS-14 | Part-time Nonseasonal |
| 15523 | 2 | SJ | 2892-AIRCRAFT ELECTRICIAN | WG-06 | Full-time Nonseasonal |
| 16075 | 3 | SJ | 2892-AIRCRAFT ELECTRICIAN | WG-07 | Full-time Nonseasonal |
| 16454 | 2 | SC | 5378-POWERED SUPPORT SYSTEMS MECHANIC | WG-06 | Full-time Nonseasonal |
| 16512 | 1 | SJ | 8602-AIRCRAFT ENGINE MECHANIC | WG-06 | Full-time Nonseasonal |
| 16691 | 2 | SJ | 0132-INTELLIGENCE | GS-04 | Full-time Nonseasonal |
| 17344 | 1 | SJ | 2101-TRANSPORTATION SPECIALIST | GS-07 | Intermittent Nonseasonal |
| 17376 | 4 | SJ | 0335-COMPUTER CLERK AND ASSISTANT | GS-07 | Part-time Nonseasonal |
| 17426 | 3 | SC | 0335-COMPUTER CLERK AND ASSISTANT | GS-06 | Intermittent Nonseasonal |
| 17464 | 3 | SJ | 8852-AIRCRAFT MECHANIC | WG-04 | Full-time Nonseasonal |
| 17763 | 1 | SJ | 4818-AIRCRAFT SURVIVAL FLIGHT EQUIPMENT REPAIR | WG-10 | Part-time Nonseasonal |
| 19309 | 4 | SD | 0701-VETERINARY MEDICAL SCIENCE | GM-15 | Full-time Nonseasonal |
| 19312 | 3 | SD | 0410-ZOOLOGY | ST-00 | Full-time Nonseasonal |
| 19704 | 2 | SJ | 3511-LABORATORY WORKING | WG-01 | Part-time Nonseasonal |
| 19768 | 2 | SD | 0435-PLANT PHYSIOLOGY | GM-15 | Full-time Nonseasonal |
| 20138 | 3 | SC | 0802-ENGINEERING TECHNICAL | GS-03 | Part-time Nonseasonal |
| 20285 | 4 | SC | 3566-CUSTODIAL WORKING | WG-01 | Intermittent Seasonal |
| 20720 | 2 | SC | 0135-FOREIGN AGRICULTURAL AFFAIRS | FP-03 | Full-time Nonseasonal |
| 20754 | 4 | SJ | 0119-ECONOMICS ASSISTANT | GS-03 | Full-time Seasonal |
| 20760 | 4 | SJ | 0399-ADMINISTRATION AND OFFICE SUPPORT STUDENT... | GS-04 | Full-time Seasonal |
| 20777 | 3 | SD | 0135-FOREIGN AGRICULTURAL AFFAIRS | FE-01 | Full-time Nonseasonal |
| 20878 | 1 | SJ | 0189-RECREATION AID AND ASSISTANT | GS-01 | Full-time Nonseasonal |
| 20928 | 1 | SJ | 0462-FORESTRY TECHNICIAN | GS-04 | Intermittent Seasonal |
| 21599 | 3 | SJ | 0455-RANGE TECHNICIAN | GS-05 | Part-time Nonseasonal |
| 23681 | 1 | SJ | 0102-SOCIAL SCIENCE AID AND TECHNICIAN | GS-03 | Full-time Nonseasonal |
| 24266 | 1 | SI | 8610-SMALL ENGINE MECHANIC | WG-06 | Full-time Seasonal |
| 24310 | 3 | SJ | 0455-RANGE TECHNICIAN | GS-06 | Intermittent Nonseasonal |
| 26446 | 1 | SC | 0304-INFORMATION RECEPTIONIST | GS-04 | Intermittent Nonseasonal |
| 27067 | 1 | SJ | 0455-RANGE TECHNICIAN | GS-05 | Intermittent Seasonal |
| 29585 | 2 | SD | 1071-AUDIOVISUAL PRODUCTION | GM-13 | Full-time Nonseasonal |
| 29689 | 2 | SJ | 0802-ENGINEERING TECHNICAL | GS-06 | Part-time Seasonal |
| 29724 | 2 | SJ | 0462-FORESTRY TECHNICIAN | GS-01 | Full-time Nonseasonal |
| 29878 | 1 | SJ | 1001-GENERAL ARTS AND INFORMATION | GS-05 | Intermittent Seasonal |
| 30106 | 1 | SJ | 5201-MISCELLANEOUS OCCUPATIONS | WG-05 | Intermittent Nonseasonal |
| 30137 | 3 | SA | 0430-BOTANY | GS-07 | Full-time Nonseasonal |
| 30156 | 2 | SJ | 0102-SOCIAL SCIENCE AID AND TECHNICIAN | GS-04 | Intermittent Nonseasonal |
| 30803 | 4 | SC | 0189-RECREATION AID AND ASSISTANT | GS-04 | Intermittent Nonseasonal |
| 30817 | 3 | SJ | 1341-METEOROLOGICAL TECHNICIAN | GS-08 | Part-time Nonseasonal |
| 32058 | 1 | SJ | 1371-CARTOGRAPHIC TECHNICIAN | GS-07 | Intermittent Nonseasonal |
| 32562 | 1 | SC | 0399-ADMINISTRATION AND OFFICE SUPPORT STUDENT... | GS-02 | Full-time Seasonal |
| 33011 | 1 | SC | 0335-COMPUTER CLERK AND ASSISTANT | GS-07 | Part-time Nonseasonal |
| 33713 | 3 | SD | 4715-EXHIBITS MAKING/MODELING | WL-07 | Full-time Nonseasonal |
| 33737 | 4 | SD | 0850-ELECTRICAL ENGINEERING | GM-14 | Full-time Nonseasonal |
| 33870 | 4 | SJ | 0318-SECRETARY | GS-03 | Intermittent Nonseasonal |
| 34226 | 1 | SC | 0322-CLERK-TYPIST | GS-04 | Intermittent Nonseasonal |
| 35280 | 3 | SJ | 0486-WILDLIFE BIOLOGY | AD-00 | Intermittent Nonseasonal |
| 35308 | 3 | SJ | 1421-ARCHIVES TECHNICIAN | GS-07 | Full-time Seasonal |
| 35369 | 2 | SJ | 0326-OFFICE AUTOMATION CLERICAL AND ASSISTANCE | GS-04 | Intermittent Seasonal |
| 35683 | 3 | SC | 0421-PLANT PROTECTION TECHNICIAN | GS-05 | Intermittent Nonseasonal |
| 35733 | 4 | SJ | 1421-ARCHIVES TECHNICIAN | GS-07 | Intermittent Nonseasonal |
| 35779 | 4 | SJ | 0404-BIOLOGICAL SCIENCE TECHNICIAN | AD-00 | Part-time Seasonal |
| 36150 | 3 | SC | 1863-FOOD INSPECTION | GS-08 | Intermittent Nonseasonal |
| 36341 | 4 | SC | 1899-INVESTIGATION STUDENT TRAINEE | GS-03 | Full-time Nonseasonal |
| 36424 | 2 | SD | 0896-INDUSTRIAL ENGINEERING | GM-13 | Full-time Nonseasonal |
| 36788 | 2 | SD | 0935-ADMINISTRATIVE LAW JUDGE | AL-02 | Full-time Nonseasonal |
| 37280 | 1 | SG | 0905-GENERAL ATTORNEY | FE-02 | Full-time Nonseasonal |
| 37464 | 3 | SJ | 1140-TRADE SPECIALIST | GS-15 | Intermittent Nonseasonal |
| 37478 | 4 | SG | 0130-FOREIGN AFFAIRS | FE-03 | Full-time Nonseasonal |
| 37721 | 1 | SC | 0809-CONSTRUCTION CONTROL TECHNICAL | NJ-03 | Full-time Nonseasonal |
| 37845 | 4 | SC | 0301-MISCELLANEOUS ADMINISTRATION AND PROGRAM | NH-02 | Part-time Nonseasonal |
| 38023 | 1 | SC | 0318-SECRETARY | NK-02 | Part-time Nonseasonal |
| 38509 | 1 | SC | 0184-SOCIOLOGY | GG-12 | Full-time Nonseasonal |
| 38675 | 2 | SD | 0896-INDUSTRIAL ENGINEERING | GG-13 | Full-time Nonseasonal |
| 39900 | 3 | SJ | 5803-HEAVY MOBILE EQUIPMENT MECHANIC | WG-08 | Intermittent Nonseasonal |
| 40629 | 4 | SC | 6904-TOOLS AND PARTS ATTENDING | WG-02 | Full-time Nonseasonal |
| 41443 | 4 | SD | 5407-ELECTRICAL POWER CONTROLLING | WG-08 | Full-time Nonseasonal |
| 41565 | 2 | SJ | 0085-SECURITY GUARD | GS-06 | Part-time Nonseasonal |
| 41658 | 1 | SJ | 5705-TRACTOR OPERATING | WL-04 | Intermittent Nonseasonal |
| 41759 | 1 | SD | 6610-SMALL ARMS REPAIRING | WL-09 | Full-time Nonseasonal |
| 41774 | 1 | SI | 0072-FINGERPRINT IDENTIFICATION | GS-09 | Full-time Nonseasonal |
| 41961 | 1 | SC | 0085-SECURITY GUARD | GS-03 | Intermittent Nonseasonal |
| 42017 | 3 | SJ | 5716-ENGINEERING EQUIPMENT OPERATING | WL-08 | Intermittent Nonseasonal |
| 42082 | 1 | SJ | 5784-RIVERBOAT OPERATING | XH-14 | Full-time Seasonal |
| 42267 | 1 | SC | 0802-ENGINEERING TECHNICAL | GS-10 | Intermittent Nonseasonal |
| 42308 | 3 | SJ | 6907-MATERIALS HANDLER | WG-06 | Intermittent Nonseasonal |
| 42324 | 1 | SJ | 5786-SMALL CRAFT OPERATING | WG-08 | Intermittent Nonseasonal |
| 42328 | 1 | SC | 0856-ELECTRONICS TECHNICAL | GS-09 | Intermittent Nonseasonal |
| 42414 | 2 | SC | 1699-EQUIPMENT AND FACILITIES MANAGEMENT STUDE... | GS-03 | Full-time Nonseasonal |
| 42653 | 4 | SC | 2805-ELECTRICIAN | WY-10 | Intermittent Nonseasonal |
| 42718 | 4 | SJ | 0899-ENGINEERING AND ARCHITECTURE STUDENT TRAINEE | DE-02 | Part-time Nonseasonal |
| 42750 | 4 | SJ | 5701-MISC TRANSPORTATION/MOBILE EQUIPMENT OPER | XF-01 | Full-time Nonseasonal |
| 42799 | 4 | SJ | 0899-ENGINEERING AND ARCHITECTURE STUDENT TRAINEE | DE-01 | Intermittent Nonseasonal |
| 42910 | 3 | SC | 0499-BIOLOGICAL SCIENCE STUDENT TRAINEE | DB-01 | Full-time Nonseasonal |
| 43219 | 3 | SJ | 5407-ELECTRICAL POWER CONTROLLING | WB-00 | Part-time Nonseasonal |
| 43234 | 3 | SJ | 5701-MISC TRANSPORTATION/MOBILE EQUIPMENT OPER | WG-03 | Part-time Nonseasonal |
| 43355 | 3 | SJ | 0301-MISCELLANEOUS ADMINISTRATION AND PROGRAM | DJ-05 | Intermittent Nonseasonal |
| 43595 | 1 | SJ | 5426-LOCK AND DAM OPERATING | WY-03 | Full-time Nonseasonal |
| 43739 | 2 | SF | 7404-COOKING | XH-06 | Full-time Seasonal |
| 44192 | 3 | SJ | 1599-MATHEMATICS AND STATISTICS STUDENT TRAINEE | DB-01 | Part-time Nonseasonal |
| 44297 | 4 | SD | 1530-STATISTICS | DB-04 | Part-time Nonseasonal |
| 44406 | 2 | SC | 0899-ENGINEERING AND ARCHITECTURE STUDENT TRAINEE | GS-05 | Intermittent Nonseasonal |
| 44419 | 2 | SA | 0890-AGRICULTURAL ENGINEERING | DB-02 | Full-time Nonseasonal |
| 44469 | 2 | SK | 7401-MISC FOOD PREPARATION AND SERVING | XH-07 | Full-time Seasonal |
| 44784 | 2 | SI | 5782-SHIP OPERATING | XH-13 | Full-time Seasonal |
| 44788 | 1 | SJ | 7404-COOKING | XF-05 | Full-time Nonseasonal |
| 44901 | 2 | SC | 1640-FACILITY OPERATIONS SERVICES | GS-09 | Intermittent Nonseasonal |
| 44952 | 2 | SC | 5782-SHIP OPERATING | XH-13 | Full-time Seasonal |
| 45157 | 3 | SD | 0808-ARCHITECTURE | DB-05 | Full-time Nonseasonal |
| 45354 | 2 | SD | 5725-CRANE OPERATING | XH-12 | Full-time Nonseasonal |
| 45458 | 1 | SJ | 3703-WELDING | WG-10 | Intermittent Nonseasonal |
| 45537 | 1 | SK | 0544-CIVILIAN PAY | GS-06 | Full-time Seasonal |
| 50004 | 3 | SC | 0682-DENTAL HYGIENE | GS-07 | Intermittent Nonseasonal |
| 50228 | 1 | SJ | 0560-BUDGET ANALYSIS | DJ-03 | Part-time Nonseasonal |
| 50820 | 3 | SC | 0401-GENERAL NATURAL RESOURCES MANAGEMENT AND ... | DB-02 | Part-time Nonseasonal |
| 51222 | 3 | SC | 0186-SOCIAL SERVICES AID AND ASSISTANT | GS-08 | Part-time Nonseasonal |
| 51430 | 1 | SC | 1712-TRAINING INSTRUCTION | DJ-03 | Full-time Nonseasonal |
| 51929 | 1 | SC | 0089-EMERGENCY MANAGEMENT SPECIALIST | DJ-04 | Full-time Nonseasonal |
| 52912 | 2 | SC | 0085-SECURITY GUARD | GS-12 | Full-time Nonseasonal |
| 53771 | 3 | SC | 6610-SMALL ARMS REPAIRING | WG-07 | Full-time Nonseasonal |
| 54340 | 1 | SJ | 1035-PUBLIC AFFAIRS | GS-06 | Full-time Nonseasonal |
| 55115 | 3 | SJ | 5801-MISC TRANSPORTATION/MOBILE EQUIPMT MAINTNE | WG-08 | Intermittent Nonseasonal |
| 55382 | 4 | SC | 8810-AIRCRAFT PROPELLER MECHANIC | WG-08 | Full-time Nonseasonal |
| 55575 | 4 | SC | 0203-HUMAN RESOURCES ASSISTANCE | GS-05 | Intermittent Nonseasonal |
| 55689 | 4 | SJ | 2604-ELECTRONICS MECHANIC | WG-12 | Part-time Nonseasonal |
| 57336 | 3 | SJ | 5413-FUEL DISTRIBUTION SYSTEM OPERATING | WG-05 | Intermittent Nonseasonal |
| 57671 | 1 | SJ | 5801-MISC TRANSPORTATION/MOBILE EQUIPMT MAINTNE | WG-08 | Intermittent Nonseasonal |
| 59155 | 3 | SD | 3101-MISC FABRIC AND LEATHER WORK | WS-11 | Full-time Nonseasonal |
| 59259 | 4 | SC | 0399-ADMINISTRATION AND OFFICE SUPPORT STUDENT... | AD-00 | Full-time Nonseasonal |
| 60044 | 3 | SA | 6610-SMALL ARMS REPAIRING | WL-06 | Full-time Nonseasonal |
| 60716 | 4 | SJ | 0101-SOCIAL SCIENCE | AD-00 | Part-time Nonseasonal |
| 61107 | 2 | SD | 5201-MISCELLANEOUS OCCUPATIONS | WS-15 | Full-time Nonseasonal |
| 61239 | 2 | SI | 2601-MISC ELECTRONIC EQUIPMT INSTALL & MAINTNE | WG-04 | Full-time Nonseasonal |
| 62578 | 4 | SA | 2608-ELECTRONIC DIGITAL COMPUTER MECHANIC | WL-10 | Full-time Nonseasonal |
| 63872 | 3 | SF | 2005-SUPPLY CLERICAL AND TECHNICIAN | GS-04 | Full-time Seasonal |
| 63902 | 3 | SF | 2610-ELECTRONIC INTEGRATED SYSTEMS MECHANIC | WT-00 | Full-time Nonseasonal |
| 64021 | 2 | SD | 3105-FABRIC WORKING | WL-11 | Full-time Nonseasonal |
| 64348 | 3 | SC | 3101-MISC FABRIC AND LEATHER WORK | WG-01 | Part-time Nonseasonal |
| 64386 | 3 | SJ | 2299-INFORMATION TECHNOLOGY STUDENT TRAINEE | DE-01 | Part-time Nonseasonal |
| 64539 | 2 | SA | 1550-COMPUTER SCIENCE | DB-03 | Part-time Nonseasonal |
| 64565 | 2 | SC | 0830-MECHANICAL ENGINEERING | DB-02 | Part-time Nonseasonal |
| 64776 | 2 | SJ | 0301-MISCELLANEOUS ADMINISTRATION AND PROGRAM | DJ-04 | Part-time Nonseasonal |
| 65181 | 4 | SC | 1510-ACTUARIAL SCIENCE | GS-15 | Intermittent Nonseasonal |
| 65336 | 1 | SC | 0110-ECONOMIST | AD-00 | Intermittent Nonseasonal |
| 65528 | 1 | SA | 0399-ADMINISTRATION AND OFFICE SUPPORT STUDENT... | ZA-02 | Full-time Nonseasonal |
| 65673 | 2 | SD | 1361-NAVIGATIONAL INFORMATION | ZA-03 | Full-time Nonseasonal |
| 65698 | 3 | SC | 2299-INFORMATION TECHNOLOGY STUDENT TRAINEE | ZP-01 | Full-time Nonseasonal |
| 65736 | 3 | SD | 0817-SURVEY TECHNICAL | ZT-02 | Full-time Nonseasonal |
| 65783 | 3 | SD | 5786-SMALL CRAFT OPERATING | WG-08 | Part-time Nonseasonal |
| 65909 | 2 | SK | 1530-STATISTICS | ZP-04 | Intermittent Nonseasonal |
| 65913 | 4 | SJ | 0410-ZOOLOGY | ZP-02 | Full-time Nonseasonal |
| 65949 | 1 | SD | 1382-FOOD TECHNOLOGY | ZP-05 | Full-time Nonseasonal |
| 66022 | 2 | SC | 9932-FIRST ASSISTANT ENGINEER | WM-11 | Full-time Nonseasonal |
| 66070 | 2 | SA | 0505-FINANCIAL MANAGEMENT | ZA-05 | Full-time Nonseasonal |
| 66108 | 2 | SA | 0361-EQUAL OPPORTUNITY ASSISTANCE | ZS-04 | Full-time Nonseasonal |
| 66142 | 3 | SK | 0301-MISCELLANEOUS ADMINISTRATION AND PROGRAM | ZA-05 | Part-time Nonseasonal |
| 66160 | 1 | SC | 0401-GENERAL NATURAL RESOURCES MANAGEMENT AND ... | ZP-02 | Full-time Seasonal |
| 66266 | 4 | SD | 1016-MUSEUM SPECIALIST AND TECHNICIAN | ZA-03 | Full-time Nonseasonal |
| 66322 | 3 | SJ | 1140-TRADE SPECIALIST | ED-00 | Intermittent Nonseasonal |
| 66328 | 2 | SD | 1801-GENERAL INSPECTION, INVESTIGATION, ENFORC... | GM-15 | Full-time Nonseasonal |
| 66555 | 2 | SC | 1224-PATENT EXAMINING | GS-09 | Full-time Seasonal |
| 66966 | 3 | SC | 1222-PATENT ATTORNEY | AD-00 | Intermittent Nonseasonal |
| 67005 | 1 | SC | 1299-COPYRIGHT AND PATENT STUDENT TRAINEE | GS-04 | Full-time Nonseasonal |
| 67046 | 2 | SC | 1320-CHEMISTRY | ZP-04 | Full-time Seasonal |
| 67055 | 2 | SC | 0341-ADMINISTRATIVE OFFICER | ZA-02 | Part-time Nonseasonal |
| 67061 | 3 | SA | 0203-HUMAN RESOURCES ASSISTANCE | ZS-04 | Part-time Nonseasonal |
| 67064 | 3 | SC | 0201-HUMAN RESOURCES MANAGEMENT | ZA-03 | Intermittent Nonseasonal |
| 67109 | 4 | SJ | 2210-INFORMATION TECHNOLOGY MANAGEMENT | ZP-05 | Intermittent Nonseasonal |
| 67139 | 4 | SC | 1310-PHYSICS | ZP-03 | Part-time Nonseasonal |
| 67145 | 4 | SC | 0342-SUPPORT SERVICES ADMINISTRATION | ZS-02 | Full-time Nonseasonal |
| 67162 | 4 | SK | 0809-CONSTRUCTION CONTROL TECHNICAL | ZT-02 | Full-time Nonseasonal |
| 67169 | 3 | SC | 0804-FIRE PROTECTION ENGINEERING | ZP-03 | Full-time Nonseasonal |
| 67178 | 4 | SC | 0342-SUPPORT SERVICES ADMINISTRATION | ZS-03 | Full-time Nonseasonal |
| 67561 | 1 | SD | 1531-STATISTICAL ASSISTANT | GG-05 | Full-time Seasonal |
| 67840 | 1 | SD | 1530-STATISTICS | GG-15 | Full-time Nonseasonal |
| 69645 | 3 | SA | 0201-HUMAN RESOURCES MANAGEMENT | GS-07 | Full-time Seasonal |
| 69678 | 3 | SC | 1371-CARTOGRAPHIC TECHNICIAN | GS-04 | Full-time Seasonal |
| 70986 | 2 | SA | 1529-MATHEMATICAL STATISTICS | GS-11 | Full-time Seasonal |
| 72229 | 4 | SC | 1099-INFORMATION AND ARTS STUDENT TRAINEE | CT-04 | Full-time Nonseasonal |
| 72291 | 2 | SA | 0132-INTELLIGENCE | CU-15 | Full-time Nonseasonal |
| 72302 | 2 | SD | 0905-GENERAL ATTORNEY | CU-14 | Part-time Nonseasonal |
| 72317 | 3 | SD | 0580-CREDIT UNION EXAMINER | CU-14 | Part-time Nonseasonal |
| 72323 | 3 | SA | 0260-EQUAL EMPLOYMENT OPPORTUNITY | CU-15 | Full-time Nonseasonal |
| 72335 | 1 | SD | 0510-ACCOUNTING | CU-15 | Full-time Nonseasonal |
| 72337 | 2 | SD | 1102-CONTRACTING | CU-13 | Full-time Nonseasonal |
| 72428 | 2 | SC | 1102-CONTRACTING | NH-04 | Part-time Job Sharer Nonseasonal |
| 73089 | 2 | SC | 1082-WRITING AND EDITING | AD-01 | Full-time Nonseasonal |
| 73246 | 3 | SJ | 0203-HUMAN RESOURCES ASSISTANCE | GS-07 | Intermittent Nonseasonal |
| 73318 | 2 | SC | 1999-QUALITY INSPECTION STUDENT TRAINEE | GS-03 | Full-time Nonseasonal |
| 74032 | 4 | SJ | 1107-PROPERTY DISPOSAL CLERICAL AND TECHNICIAN | GS-04 | Full-time Nonseasonal |
| 74457 | 3 | SC | 2032-PACKAGING | GS-14 | Part-time Nonseasonal |
| 75550 | 1 | SD | 0080-SECURITY ADMINISTRATION | IE-00 | Full-time Nonseasonal |
| 75574 | 4 | SD | 0306-GOVERNMENT INFORMATION SPECIALIST | GG-14 | Full-time Nonseasonal |
| 75624 | 4 | SC | 0806-MATERIALS ENGINEERING | AD-00 | Full-time Nonseasonal |
| 75634 | 1 | SJ | 0830-MECHANICAL ENGINEERING | EE-00 | Full-time Nonseasonal |
| 75813 | 1 | SC | 0631-OCCUPATIONAL THERAPIST | AD-16 | Full-time Seasonal |
| 75845 | 4 | SH | 1710-EDUCATION AND VOCATIONAL TRAINING | AD-13 | Part-time Seasonal |
| 76307 | 4 | SC | 0665-SPEECH PATHOLOGY AND AUDIOLOGY | AD-14 | Full-time Seasonal |
| 76420 | 4 | SH | 1710-EDUCATION AND VOCATIONAL TRAINING | AD-14 | Part-time Nonseasonal |
| 76667 | 3 | SC | 0640-HEALTH AID AND TECHNICIAN | GS-04 | Part-time Seasonal |
| 76735 | 4 | SJ | 0610-NURSE | AD-11 | Intermittent Nonseasonal |
| 76771 | 3 | SC | 1710-EDUCATION AND VOCATIONAL TRAINING | AD-00 | Part-time Seasonal |
| 77484 | 2 | SC | 0808-ARCHITECTURE | NH-02 | Full-time Nonseasonal |
| 77695 | 3 | SJ | 7408-FOOD SERVICE WORKING | WL-02 | Part-time Nonseasonal |
| 77887 | 1 | SF | 1101-GENERAL BUSINESS AND INDUSTRY | GS-02 | Full-time Nonseasonal |
| 77916 | 1 | SJ | 1101-GENERAL BUSINESS AND INDUSTRY | GS-01 | Full-time Nonseasonal |
| 80303 | 4 | SC | 0599-FINANCIAL MANAGEMENT STUDENT TRAINEE | GS-05 | Full-time Seasonal |
| 83224 | 4 | SD | 1101-GENERAL BUSINESS AND INDUSTRY | AD-02 | Full-time Nonseasonal |
| 83250 | 2 | SD | 0346-LOGISTICS MANAGEMENT | AD-03 | Full-time Nonseasonal |
| 83404 | 1 | SA | 1799-EDUCATION STUDENT TRAINEE | NJ-02 | Full-time Nonseasonal |
| 83422 | 2 | SC | 0301-MISCELLANEOUS ADMINISTRATION AND PROGRAM | NH-04 | Intermittent Nonseasonal |
| 83425 | 2 | SD | 0896-INDUSTRIAL ENGINEERING | AD-12 | Full-time Nonseasonal |
| 83442 | 1 | SJ | 1701-GENERAL EDUCATION AND TRAINING | AD-22 | Intermittent Nonseasonal |
| 84642 | 4 | SC | 1060-PHOTOGRAPHY | GS-12 | Part-time Nonseasonal |
| 85721 | 3 | SD | 0610-NURSE | GL-10 | Part-time Nonseasonal |
| 85965 | 2 | SC | 1199-BUSINESS AND INDUSTRY STUDENT TRAINEE | GL-04 | Part-time Nonseasonal |
| 86905 | 4 | SJ | 0299-HUMAN RESOURCES MANAGEMENT STUDENT TRAINEE | GL-05 | Full-time Nonseasonal |
| ... | ... | ... | ... | ... | ... |
| 138901 | 2 | SC | 1550-COMPUTER SCIENCE | EG-00 | Intermittent Nonseasonal |
| 138906 | 3 | SJ | 0018-SAFETY AND OCCUPATIONAL HEALTH MANAGEMENT | GS-15 | Intermittent Nonseasonal |
| 138918 | 3 | SJ | 1520-MATHEMATICS | EE-00 | Intermittent Nonseasonal |
| 138947 | 2 | SC | 1520-MATHEMATICS | EG-00 | Intermittent Nonseasonal |
| 138988 | 1 | SD | 0170-HISTORY | AD-04 | Full-time Nonseasonal |
| 139029 | 2 | SC | 1040-LANGUAGE SPECIALIST | GS-07 | Part-time Nonseasonal |
| 139618 | 1 | SJ | 1550-COMPUTER SCIENCE | EF-00 | Intermittent Nonseasonal |
| 140156 | 4 | SJ | 0020-COMMUNITY PLANNING | EE-00 | Full-time Nonseasonal |
| 140167 | 1 | SC | 1010-EXHIBITS SPECIALIST | GS-11 | Part-time Nonseasonal |
| 140258 | 4 | SJ | 1499-LIBRARY AND ARCHIVES STUDENT TRAINEE | GS-02 | Part-time Nonseasonal |
| 140910 | 1 | SK | 1301-GENERAL PHYSICAL SCIENCE | AJ-00 | Full-time Nonseasonal |
| 140921 | 3 | SD | 0080-SECURITY ADMINISTRATION | EG-00 | Intermittent Nonseasonal |
| 140948 | 4 | SC | 0999-LEGAL OCCUPATIONS STUDENT TRAINEE | GG-07 | Full-time Nonseasonal |
| 140949 | 4 | SJ | 0899-ENGINEERING AND ARCHITECTURE STUDENT TRAINEE | GG-05 | Full-time Nonseasonal |
| 140981 | 4 | SC | 0899-ENGINEERING AND ARCHITECTURE STUDENT TRAINEE | GG-05 | Full-time Nonseasonal |
| 140984 | 4 | SC | 0899-ENGINEERING AND ARCHITECTURE STUDENT TRAINEE | GG-09 | Full-time Nonseasonal |
| 140987 | 4 | SC | 0399-ADMINISTRATION AND OFFICE SUPPORT STUDENT... | GG-03 | Full-time Nonseasonal |
| 140992 | 4 | SC | 0999-LEGAL OCCUPATIONS STUDENT TRAINEE | GG-09 | Full-time Nonseasonal |
| 141000 | 4 | SJ | 0343-MANAGEMENT AND PROGRAM ANALYSIS | GG-07 | Part-time Nonseasonal |
| 141005 | 4 | SC | 0399-ADMINISTRATION AND OFFICE SUPPORT STUDENT... | GG-05 | Full-time Nonseasonal |
| 141010 | 4 | SC | 0399-ADMINISTRATION AND OFFICE SUPPORT STUDENT... | GG-07 | Full-time Nonseasonal |
| 141027 | 4 | SC | 2299-INFORMATION TECHNOLOGY STUDENT TRAINEE | GG-07 | Full-time Nonseasonal |
| 141029 | 4 | SC | 1399-PHYSICAL SCIENCE STUDENT TRAINEE | GG-07 | Full-time Nonseasonal |
| 141056 | 2 | SD | 0482-FISH BIOLOGY | GG-15 | Full-time Nonseasonal |
| 141057 | 4 | SC | 0399-ADMINISTRATION AND OFFICE SUPPORT STUDENT... | GG-04 | Full-time Nonseasonal |
| 141071 | 4 | SJ | 0399-ADMINISTRATION AND OFFICE SUPPORT STUDENT... | GG-05 | Full-time Nonseasonal |
| 141081 | 4 | SJ | 0599-FINANCIAL MANAGEMENT STUDENT TRAINEE | GG-05 | Part-time Nonseasonal |
| 141100 | 4 | SJ | 0599-FINANCIAL MANAGEMENT STUDENT TRAINEE | GG-05 | Full-time Nonseasonal |
| 141128 | 3 | SJ | 0801-GENERAL ENGINEERING | GG-15 | Intermittent Nonseasonal |
| 141145 | 2 | SJ | 0399-ADMINISTRATION AND OFFICE SUPPORT STUDENT... | GG-05 | Full-time Nonseasonal |
| 141150 | 1 | SJ | 2299-INFORMATION TECHNOLOGY STUDENT TRAINEE | GG-09 | Full-time Nonseasonal |
| 141153 | 3 | SD | 1301-GENERAL PHYSICAL SCIENCE | GG-15 | Part-time Nonseasonal |
| 141852 | 3 | SD | 1811-CRIMINAL INVESTIGATION | IE-00 | Full-time Nonseasonal |
| 141986 | 2 | SD | 0332-COMPUTER OPERATION | NC-03 | Full-time Nonseasonal |
| 141991 | 3 | SC | 0899-ENGINEERING AND ARCHITECTURE STUDENT TRAINEE | NR-01 | Full-time Nonseasonal |
| 142033 | 2 | SA | 2091-SALES STORE CLERICAL | NC-01 | Full-time Nonseasonal |
| 142047 | 1 | SD | 0690-INDUSTRIAL HYGIENE | NO-04 | Full-time Nonseasonal |
| 142061 | 3 | SC | 0999-LEGAL OCCUPATIONS STUDENT TRAINEE | NC-01 | Full-time Nonseasonal |
| 142069 | 4 | SC | 1399-PHYSICAL SCIENCE STUDENT TRAINEE | NP-01 | Full-time Nonseasonal |
| 142072 | 4 | SJ | 0855-ELECTRONICS ENGINEERING | NP-03 | Intermittent Nonseasonal |
| 142075 | 3 | SC | 0899-ENGINEERING AND ARCHITECTURE STUDENT TRAINEE | NR-01 | Part-time Nonseasonal |
| 142111 | 2 | SA | 0809-CONSTRUCTION CONTROL TECHNICAL | NR-03 | Full-time Nonseasonal |
| 142119 | 4 | SC | 1310-PHYSICS | NP-04 | Part-time Nonseasonal |
| 142154 | 3 | SC | 0899-ENGINEERING AND ARCHITECTURE STUDENT TRAINEE | NP-02 | Part-time Nonseasonal |
| 142163 | 1 | SJ | 1411-LIBRARY TECHNICIAN | NC-01 | Full-time Nonseasonal |
| 142168 | 2 | SC | 0086-SECURITY CLERICAL AND ASSISTANCE | NC-01 | Full-time Nonseasonal |
| 142203 | 1 | SJ | 1399-PHYSICAL SCIENCE STUDENT TRAINEE | NR-02 | Part-time Nonseasonal |
| 143128 | 2 | SC | 0638-RECREATION/CREATIVE ARTS THERAPIST | GS-11 | Part-time Nonseasonal |
| 143264 | 1 | SD | 6901-MISC WAREHOUSING AND STOCK HANDLING | WG-03 | Part-time Nonseasonal |
| 143491 | 4 | SC | 0895-INDUSTRIAL ENGINEERING TECHNICAL | GS-04 | Full-time Nonseasonal |
| 143699 | 3 | SJ | 2210-INFORMATION TECHNOLOGY MANAGEMENT | DS-01 | Full-time Nonseasonal |
| 143939 | 3 | SK | 1311-PHYSICAL SCIENCE TECHNICIAN | DT-04 | Part-time Nonseasonal |
| 144126 | 4 | SC | 0801-GENERAL ENGINEERING | DP-05 | Part-time Nonseasonal |
| 144158 | 2 | SC | 0830-MECHANICAL ENGINEERING | NM-03 | Full-time Nonseasonal |
| 144164 | 2 | SA | 1103-INDUSTRIAL PROPERTY MANAGEMENT | DA-04 | Full-time Nonseasonal |
| 144282 | 3 | SJ | 2299-INFORMATION TECHNOLOGY STUDENT TRAINEE | DS-01 | Full-time Nonseasonal |
| 144314 | 3 | SJ | 0899-ENGINEERING AND ARCHITECTURE STUDENT TRAINEE | DP-03 | Full-time Nonseasonal |
| 144465 | 3 | SC | 1199-BUSINESS AND INDUSTRY STUDENT TRAINEE | DG-01 | Full-time Nonseasonal |
| 144579 | 2 | SC | 1320-CHEMISTRY | DP-02 | Part-time Nonseasonal |
| 144679 | 3 | SD | 0303-MISCELLANEOUS CLERK AND ASSISTANT | DG-06 | Full-time Nonseasonal |
| 144866 | 1 | SC | 0899-ENGINEERING AND ARCHITECTURE STUDENT TRAINEE | DT-01 | Part-time Nonseasonal |
| 144902 | 4 | SJ | 1710-EDUCATION AND VOCATIONAL TRAINING | AD-01 | Part-time Nonseasonal |
| 145001 | 4 | SJ | 1710-EDUCATION AND VOCATIONAL TRAINING | AD-03 | Part-time Nonseasonal |
| 145028 | 3 | SC | 1710-EDUCATION AND VOCATIONAL TRAINING | AD-01 | Full-time Seasonal |
| 145194 | 1 | SC | 0006-CORRECTIONAL INSTITUTION ADMINISTRATION | GS-13 | Part-time Nonseasonal |
| 146139 | 1 | SD | 6641-ORDNANCE EQUIPMENT MECHANIC | WG-12 | Full-time Nonseasonal |
| 146276 | 2 | SD | 1515-OPERATIONS RESEARCH | ND-05 | Part-time Nonseasonal |
| 146408 | 4 | SJ | 0899-ENGINEERING AND ARCHITECTURE STUDENT TRAINEE | GS-04 | Full-time Seasonal |
| 146441 | 4 | SC | 0899-ENGINEERING AND ARCHITECTURE STUDENT TRAINEE | NT-01 | Full-time Nonseasonal |
| 146517 | 4 | SC | 0899-ENGINEERING AND ARCHITECTURE STUDENT TRAINEE | GS-04 | Full-time Seasonal |
| 146526 | 3 | SD | 4301-MISCELLANEOUS PLIABLE MATERIALS WORK | WG-11 | Full-time Nonseasonal |
| 146559 | 2 | SC | 0415-TOXICOLOGY | ND-04 | Part-time Nonseasonal |
| 146714 | 4 | SK | 1102-CONTRACTING | NT-05 | Part-time Nonseasonal |
| 146905 | 4 | SC | 1222-PATENT ATTORNEY | NT-05 | Full-time Nonseasonal |
| 146968 | 2 | SC | 1515-OPERATIONS RESEARCH | ND-05 | Part-time Nonseasonal |
| 147275 | 3 | SD | 1521-MATHEMATICS TECHNICIAN | GS-12 | Full-time Nonseasonal |
| 147378 | 4 | SF | 3414-MACHINING | WG-04 | Full-time Nonseasonal |
| 147609 | 2 | SD | 5876-ELECTROMOTIVE EQUIPMENT MECHANIC | WG-11 | Full-time Nonseasonal |
| 148473 | 3 | SC | 0804-FIRE PROTECTION ENGINEERING | GS-13 | Part-time Nonseasonal |
| 148595 | 3 | SD | 5407-ELECTRICAL POWER CONTROLLING | WS-11 | Full-time Nonseasonal |
| 148604 | 4 | SA | 0021-COMMUNITY PLANNING TECHNICIAN | GS-04 | Full-time Nonseasonal |
| 149482 | 2 | SI | 5409-WATER TREATMENT PLANT OPERATING | WS-11 | Full-time Nonseasonal |
| 149539 | 4 | SA | 0399-ADMINISTRATION AND OFFICE SUPPORT STUDENT... | GG-07 | Full-time Nonseasonal |
| 149587 | 4 | SC | 0899-ENGINEERING AND ARCHITECTURE STUDENT TRAINEE | GS-03 | Full-time Seasonal |
| 149648 | 4 | SD | 1082-WRITING AND EDITING | GS-08 | Full-time Nonseasonal |
| 149876 | 3 | SD | 5413-FUEL DISTRIBUTION SYSTEM OPERATING | WL-09 | Full-time Nonseasonal |
| 150646 | 3 | SJ | 0801-GENERAL ENGINEERING | EE-00 | Full-time Nonseasonal |
| 150728 | 1 | SJ | 9936-ENGINE MIDSHIPMAN | WM-21 | Full-time Nonseasonal |
| 150901 | 1 | SJ | 9917-DECK MIDSHIPMAN | WM-21 | Full-time Nonseasonal |
| 151029 | 2 | SJ | 9917-DECK MIDSHIPMAN | WM-21 | Full-time Nonseasonal |
| 151039 | 2 | SJ | 9936-ENGINE MIDSHIPMAN | WM-21 | Full-time Nonseasonal |
| 151282 | 1 | SD | 2131-FREIGHT RATE | NG-03 | Full-time Nonseasonal |
| 151366 | 1 | SA | 0260-EQUAL EMPLOYMENT OPPORTUNITY | DP-04 | Full-time Nonseasonal |
| 151369 | 1 | SI | 0361-EQUAL OPPORTUNITY ASSISTANCE | NG-02 | Full-time Nonseasonal |
| 151387 | 3 | SC | 2299-INFORMATION TECHNOLOGY STUDENT TRAINEE | NO-03 | Full-time Nonseasonal |
| 151397 | 4 | SC | 1199-BUSINESS AND INDUSTRY STUDENT TRAINEE | NG-01 | Part-time Nonseasonal |
| 151625 | 3 | SD | 0510-ACCOUNTING | NO-06 | Full-time Nonseasonal |
| 153743 | 2 | SD | 4373-MOLDING | WD-06 | Full-time Nonseasonal |
| 154266 | 1 | SD | 3802-METAL FORGING | WL-10 | Full-time Nonseasonal |
| 154284 | 3 | SC | 3359-INSTRUMENT MECHANIC | WG-01 | Full-time Seasonal |
| 154346 | 3 | SC | 0871-NAVAL ARCHITECTURE | GS-12 | Part-time Nonseasonal |
| 154627 | 3 | SC | 3801-MISCELLANEOUS METAL WORK | WG-03 | Full-time Seasonal |
| 154637 | 3 | SA | 3806-SHEET METAL MECHANIC | WG-05 | Full-time Seasonal |
| 155027 | 2 | SD | 3401-MISCELLANEOUS MACHINE TOOL WORK | WS-15 | Full-time Nonseasonal |
| 155347 | 1 | SC | 3414-MACHINING | WG-08 | Full-time Seasonal |
| 156304 | 3 | SC | 0086-SECURITY CLERICAL AND ASSISTANCE | FP-06 | Full-time Nonseasonal |
| 156307 | 4 | SD | 2130-TRAFFIC MANAGEMENT | FP-02 | Full-time Nonseasonal |
| 156310 | 4 | SC | 1087-EDITORIAL ASSISTANCE | FP-07 | Full-time Nonseasonal |
| 156321 | 2 | SA | 0905-GENERAL ATTORNEY | FP-04 | Full-time Nonseasonal |
| 156355 | 3 | SA | 0510-ACCOUNTING | FP-03 | Full-time Nonseasonal |
| 156360 | 2 | SI | 0303-MISCELLANEOUS CLERK AND ASSISTANT | FP-07 | Part-time Nonseasonal |
| 156361 | 3 | SC | 0303-MISCELLANEOUS CLERK AND ASSISTANT | FP-09 | Part-time Nonseasonal |
| 156368 | 4 | SC | 0669-MEDICAL RECORDS ADMINISTRATION | FP-05 | Full-time Nonseasonal |
| 156374 | 2 | SJ | 0303-MISCELLANEOUS CLERK AND ASSISTANT | FP-08 | Full-time Nonseasonal |
| 156382 | 4 | SC | 1702-EDUCATION AND TRAINING TECHNICIAN | FP-06 | Full-time Nonseasonal |
| 156448 | 2 | SC | 0303-MISCELLANEOUS CLERK AND ASSISTANT | FP-08 | Full-time Nonseasonal |
| 156454 | 1 | SC | 1750-INSTRUCTIONAL SYSTEMS | FP-04 | Full-time Nonseasonal |
| 156479 | 4 | SA | 0260-EQUAL EMPLOYMENT OPPORTUNITY | FP-04 | Full-time Nonseasonal |
| 156484 | 1 | SA | 0201-HUMAN RESOURCES MANAGEMENT | FP-02 | Part-time Nonseasonal |
| 157610 | 4 | SC | 2210-INFORMATION TECHNOLOGY MANAGEMENT | SK-16 | Part-time Nonseasonal |
| 157665 | 2 | SD | 1410-LIBRARIAN | SK-15 | Full-time Nonseasonal |
| 157751 | 4 | SC | 0201-HUMAN RESOURCES MANAGEMENT | SK-13 | Part-time Nonseasonal |
| 157761 | 4 | SC | 1499-LIBRARY AND ARCHIVES STUDENT TRAINEE | SK-07 | Full-time Nonseasonal |
| 157764 | 4 | SD | 0340-PROGRAM MANAGEMENT | SK-16 | Full-time Nonseasonal |
| 157782 | 1 | SJ | 0950-PARALEGAL SPECIALIST | SK-07 | Full-time Nonseasonal |
| 157790 | 2 | SC | 1750-INSTRUCTIONAL SYSTEMS | SK-16 | Full-time Nonseasonal |
| 157794 | 2 | SC | 1410-LIBRARIAN | SK-09 | Full-time Nonseasonal |
| 157795 | 3 | SI | 0080-SECURITY ADMINISTRATION | SK-17 | Full-time Nonseasonal |
| 157798 | 2 | SK | 0201-HUMAN RESOURCES MANAGEMENT | SK-14 | Part-time Nonseasonal |
| 157823 | 3 | SC | 2210-INFORMATION TECHNOLOGY MANAGEMENT | SO-01 | Full-time Nonseasonal |
| 157834 | 4 | SJ | 0501-FINANCIAL ADMINISTRATION AND PROGRAM | SK-13 | Part-time Nonseasonal |
| 158130 | 3 | SC | 0804-FIRE PROTECTION ENGINEERING | GS-07 | Full-time Nonseasonal |
| 158187 | 2 | SC | 5701-MISC TRANSPORTATION/MOBILE EQUIPMENT OPER | WL-02 | Full-time Nonseasonal |
| 158375 | 1 | SF | 0356-DATA TRANSCRIBER | GS-07 | Full-time Nonseasonal |
| 158552 | 3 | SJ | 0130-FOREIGN AFFAIRS | GS-15 | Intermittent Nonseasonal |
| 158577 | 3 | SC | 0130-FOREIGN AFFAIRS | AD-00 | Full-time Nonseasonal |
| 158579 | 3 | SJ | 2032-PACKAGING | GS-12 | Intermittent Nonseasonal |
| 158583 | 3 | SA | 0130-FOREIGN AFFAIRS | GG-14 | Full-time Nonseasonal |
| 158587 | 3 | SC | 0130-FOREIGN AFFAIRS | EF-15 | Intermittent Nonseasonal |
| 158599 | 3 | SJ | 0130-FOREIGN AFFAIRS | EF-14 | Intermittent Nonseasonal |
| 158622 | 3 | SJ | 0080-SECURITY ADMINISTRATION | GS-14 | Intermittent Nonseasonal |
| 158654 | 3 | SJ | 0130-FOREIGN AFFAIRS | GS-14 | Intermittent Nonseasonal |
| 158692 | 2 | SJ | 0318-SECRETARY | GS-10 | Intermittent Nonseasonal |
| 158775 | 2 | SD | 1008-INTERIOR DESIGN | GS-15 | Full-time Nonseasonal |
| 158847 | 3 | SJ | 0391-TELECOMMUNICATIONS | GS-11 | Intermittent Nonseasonal |
| 158884 | 3 | SD | 0150-GEOGRAPHY | ES-** | Full-time Nonseasonal |
| 158987 | 2 | SD | 0132-INTELLIGENCE | GM-13 | Full-time Nonseasonal |
| 159027 | 3 | SC | 0130-FOREIGN AFFAIRS | GS-14 | Intermittent Nonseasonal |
| 159029 | 3 | SC | 1109-GRANTS MANAGEMENT | AD-05 | Full-time Nonseasonal |
| 159042 | 3 | SJ | 1035-PUBLIC AFFAIRS | AD-05 | Full-time Nonseasonal |
| 159044 | 3 | SC | 0306-GOVERNMENT INFORMATION SPECIALIST | GS-09 | Part-time Nonseasonal |
| 159045 | 3 | SJ | 0130-FOREIGN AFFAIRS | EF-15 | Intermittent Nonseasonal |
| 159103 | 3 | SC | 0130-FOREIGN AFFAIRS | ED-15 | Intermittent Nonseasonal |
| 159147 | 3 | SK | 0130-FOREIGN AFFAIRS | GS-14 | Intermittent Nonseasonal |
| 159217 | 1 | SC | 0130-FOREIGN AFFAIRS | EF-15 | Full-time Nonseasonal |
| 159782 | 3 | SC | 0905-GENERAL ATTORNEY | AA-06 | Intermittent Nonseasonal |
| 159803 | 2 | SJ | 0901-GENERAL LEGAL AND KINDRED ADMINISTRATION | GS-09 | Intermittent Nonseasonal |
| 160700 | 3 | SC | 0260-EQUAL EMPLOYMENT OPPORTUNITY | GS-15 | Intermittent Nonseasonal |
| 160723 | 3 | SJ | 0905-GENERAL ATTORNEY | GS-12 | Intermittent Nonseasonal |
| 161612 | 2 | SC | 0998-CLAIMS ASSISTANCE AND EXAMINING | GS-07 | Intermittent Nonseasonal |
| 164022 | 2 | SI | 0105-SOCIAL INSURANCE ADMINISTRATION | GS-06 | Full-time Nonseasonal |
| 164528 | 1 | SD | 0160-CIVIL RIGHTS ANALYSIS | ES-** | Full-time Nonseasonal |
| 164537 | 3 | SC | 0020-COMMUNITY PLANNING | GS-12 | Intermittent Nonseasonal |
| 164593 | 3 | SC | 1499-LIBRARY AND ARCHIVES STUDENT TRAINEE | GS-07 | Part-time Nonseasonal |
| 164751 | 3 | SC | 0301-MISCELLANEOUS ADMINISTRATION AND PROGRAM | FJ-00 | Full-time Nonseasonal |
| 165005 | 3 | SD | 0413-PHYSIOLOGY | FV-J | Full-time Nonseasonal |
| 165047 | 4 | SD | 1825-AVIATION SAFETY | EV-02 | Full-time Nonseasonal |
| 165056 | 1 | SD | 0346-LOGISTICS MANAGEMENT | FV-G | Part-time Nonseasonal |
| 165207 | 4 | SI | 0861-AEROSPACE ENGINEERING | FG-13 | Full-time Nonseasonal |
| 165242 | 3 | SD | 1071-AUDIOVISUAL PRODUCTION | FV-G | Full-time Nonseasonal |
| 165440 | 4 | SD | 0601-GENERAL HEALTH SCIENCE | FV-G | Full-time Nonseasonal |
| 165681 | 4 | SC | 0401-GENERAL NATURAL RESOURCES MANAGEMENT AND ... | FV-G | Full-time Nonseasonal |
| 165694 | 4 | SK | 0343-MANAGEMENT AND PROGRAM ANALYSIS | FG-15 | Full-time Nonseasonal |
| 165819 | 3 | SC | 0343-MANAGEMENT AND PROGRAM ANALYSIS | FG-07 | Full-time Nonseasonal |
| 166216 | 2 | SJ | 0899-ENGINEERING AND ARCHITECTURE STUDENT TRAINEE | FV-C | Part-time Nonseasonal |
| 166649 | 2 | SD | 0401-GENERAL NATURAL RESOURCES MANAGEMENT AND ... | FV-I | Full-time Nonseasonal |
| 167164 | 3 | SD | 2010-INVENTORY MANAGEMENT | FG-09 | Full-time Nonseasonal |
| 167232 | 1 | SC | 0675-MEDICAL RECORDS TECHNICIAN | FV-G | Full-time Nonseasonal |
| 167765 | 4 | SA | 0810-CIVIL ENGINEERING | GS-07 | Full-time Seasonal |
| 167983 | 4 | SC | 0090-GUIDE | GS-01 | Full-time Nonseasonal |
| 167984 | 4 | SJ | 0090-GUIDE | GS-01 | Full-time Nonseasonal |
| 167990 | 3 | SD | 5786-SMALL CRAFT OPERATING | WL-12 | Full-time Seasonal |
| 168609 | 1 | SA | 1520-MATHEMATICS | OR-51 | Full-time Nonseasonal |
| 169045 | 4 | SC | 0356-DATA TRANSCRIBER | GS-03 | Full-time Seasonal |
| 169402 | 4 | SJ | 0356-DATA TRANSCRIBER | GS-03 | Part-time Nonseasonal |
| 169897 | 3 | SD | 0341-ADMINISTRATIVE OFFICER | IR-SM | Full-time Nonseasonal |
| 170587 | 3 | SJ | 0303-MISCELLANEOUS CLERK AND ASSISTANT | GS-02 | Full-time Seasonal |
| 170682 | 4 | SA | 0501-FINANCIAL ADMINISTRATION AND PROGRAM | GS-05 | Full-time Seasonal |
| 171044 | 3 | SC | 0303-MISCELLANEOUS CLERK AND ASSISTANT | GS-02 | Full-time Seasonal |
| 172568 | 3 | SJ | 2005-SUPPLY CLERICAL AND TECHNICIAN | GS-04 | Intermittent Nonseasonal |
| 172848 | 1 | SI | 0356-DATA TRANSCRIBER | GS-03 | Full-time Seasonal |
| 173512 | 2 | SD | 1101-GENERAL BUSINESS AND INDUSTRY | GS-09 | Part-time Job Sharer Nonseasonal |
| 174326 | 4 | SC | 0592-TAX EXAMINING | GS-05 | Part-time Seasonal |
| 175009 | 2 | SD | 1397-DOCUMENT ANALYSIS | IR-FM | Full-time Nonseasonal |
| 175175 | 2 | SC | 0399-ADMINISTRATION AND OFFICE SUPPORT STUDENT... | GS-06 | Full-time Seasonal |
| 176572 | 3 | SC | 0501-FINANCIAL ADMINISTRATION AND PROGRAM | GS-09 | Part-time Seasonal |
| 176724 | 4 | SC | 0356-DATA TRANSCRIBER | GS-02 | Full-time Seasonal |
| 177579 | 1 | SJ | 0512-INTERNAL REVENUE AGENT | GS-13 | Intermittent Nonseasonal |
| 178189 | 1 | SC | 0356-DATA TRANSCRIBER | GS-02 | Full-time Seasonal |
| 178500 | 4 | SI | 3869-METAL FORMING MACHINE OPERATING | WG-06 | Intermittent Nonseasonal |
| 178757 | 1 | SD | 0511-AUDITING | NB-07 | Full-time Nonseasonal |
| 178763 | 4 | SC | 0399-ADMINISTRATION AND OFFICE SUPPORT STUDENT... | NB-03 | Full-time Nonseasonal |
| 178811 | 3 | SC | 1160-FINANCIAL ANALYSIS | NB-06 | Part-time Nonseasonal |
| 178820 | 4 | SC | 0110-ECONOMIST | NB-06 | Part-time Nonseasonal |
| 178862 | 1 | SD | 0986-LEGAL ASSISTANCE | NB-04 | Full-time Nonseasonal |
| 178979 | 1 | SA | 0303-MISCELLANEOUS CLERK AND ASSISTANT | NB-02 | Part-time Nonseasonal |
| 179028 | 1 | SC | 0570-FINANCIAL INSTITUTION EXAMINING | NB-06 | Intermittent Nonseasonal |
| 179323 | 4 | SC | 0511-AUDITING | ES-** | Intermittent Nonseasonal |
| 180727 | 4 | SC | 0996-VETERANS CLAIMS EXAMINING | GS-10 | Part-time Nonseasonal |
| 182096 | 1 | SJ | 0503-FINANCIAL CLERICAL AND ASSISTANCE | GS-06 | Intermittent Nonseasonal |
| 182785 | 1 | SJ | 0621-NURSING ASSISTANT | AD-00 | Full-time Nonseasonal |
| 183419 | 1 | SJ | 4102-PAINTING | WB-00 | Intermittent Nonseasonal |
| 184918 | 1 | SC | 0661-PHARMACY TECHNICIAN | GS-02 | Intermittent Nonseasonal |
| 185606 | 1 | SC | 0187-SOCIAL SERVICES | GS-07 | Intermittent Nonseasonal |
| 185612 | 1 | SC | 7305-LAUNDRY MACHINE OPERATING | WL-04 | Full-time Nonseasonal |
| 185619 | 4 | SI | 0102-SOCIAL SCIENCE AID AND TECHNICIAN | GS-02 | Part-time Nonseasonal |
| 186470 | 3 | SI | 0661-PHARMACY TECHNICIAN | GS-02 | Part-time Nonseasonal |
| 187280 | 2 | SC | 0625-AUTOPSY ASSISTANT | GS-04 | Intermittent Nonseasonal |
| 188209 | 3 | SC | 7305-LAUNDRY MACHINE OPERATING | WG-04 | Part-time Nonseasonal |
| 188290 | 3 | SC | 0083-POLICE | GS-07 | Intermittent Nonseasonal |
| 189645 | 1 | SD | 1601-EQUIPMENT FACILITIES, AND SERVICES | GS-11 | Intermittent Nonseasonal |
| 190784 | 3 | SC | 0180-PSYCHOLOGY | AD-00 | Intermittent Nonseasonal |
| 191199 | 3 | SJ | 0199-SOCIAL SCIENCE STUDENT TRAINEE | AD-00 | Part-time Nonseasonal |
| 193872 | 4 | SJ | 0185-SOCIAL WORK | GS-02 | Intermittent Nonseasonal |
| 194936 | 2 | SJ | 1199-BUSINESS AND INDUSTRY STUDENT TRAINEE | GS-02 | Intermittent Nonseasonal |
| 195894 | 1 | SJ | 0401-GENERAL NATURAL RESOURCES MANAGEMENT AND ... | GS-10 | Intermittent Nonseasonal |
| 195895 | 1 | SC | 0299-HUMAN RESOURCES MANAGEMENT STUDENT TRAINEE | GS-09 | Part-time Nonseasonal |
| 196227 | 4 | SD | 7301-MISC LAUNDRY, DRY CLEANING, AND PRESSING | WS-08 | Full-time Nonseasonal |
| 196320 | 2 | SC | 0681-DENTAL ASSISTANT | GS-08 | Part-time Nonseasonal |
| 196786 | 1 | SC | 0683-DENTAL LABORATORY AID AND TECHNICIAN | GS-05 | Part-time Nonseasonal |
| 198299 | 4 | SC | 0699-MEDICAL AND HEALTH STUDENT TRAINEE | GS-02 | Full-time Nonseasonal |
| 198505 | 4 | SC | 0530-CASH PROCESSING | GS-05 | Part-time Nonseasonal |
| 199799 | 4 | SC | 0181-PSYCHOLOGY AID AND TECHNICIAN | GS-04 | Intermittent Nonseasonal |
| 200273 | 2 | SJ | 0335-COMPUTER CLERK AND ASSISTANT | GS-03 | Intermittent Nonseasonal |
| 200845 | 4 | SJ | 0525-ACCOUNTING TECHNICIAN | GS-04 | Intermittent Nonseasonal |
| 201133 | 1 | SC | 7404-COOKING | WG-07 | Full-time Nonseasonal |
| 201186 | 1 | SD | 0639-EDUCATIONAL THERAPIST | GS-09 | Full-time Nonseasonal |
| 202022 | 4 | SJ | 0601-GENERAL HEALTH SCIENCE | GS-05 | Intermittent Nonseasonal |
| 202948 | 2 | SD | 0637-MANUAL ARTS THERAPIST | GS-11 | Full-time Nonseasonal |
| 203694 | 2 | SC | 0530-CASH PROCESSING | VC-02 | Intermittent Nonseasonal |
| 203728 | 2 | SC | 5703-MOTOR VEHICLE OPERATING | WG-02 | Part-time Nonseasonal |
| 203929 | 3 | SC | 0669-MEDICAL RECORDS ADMINISTRATION | GS-11 | Intermittent Nonseasonal |
| 204060 | 2 | SD | 6901-MISC WAREHOUSING AND STOCK HANDLING | WD-07 | Full-time Nonseasonal |
| 204563 | 2 | SJ | 0644-MEDICAL TECHNOLOGIST | GS-07 | Intermittent Nonseasonal |
| 206370 | 1 | SJ | 0670-HEALTH SYSTEM ADMINISTRATION | AD-00 | Intermittent Nonseasonal |
| 207091 | 1 | SJ | 0605-NURSE ANESTHETIST (TITLE 38) | AD-00 | Part-time Nonseasonal |
| 210811 | 3 | SD | 4010-PRESCRIPTION EYEGLASS MAKING | WL-09 | Full-time Nonseasonal |
| 211162 | 3 | SC | 0699-MEDICAL AND HEALTH STUDENT TRAINEE | GS-06 | Full-time Nonseasonal |
| 211308 | 1 | SC | 0645-MEDICAL TECHNICIAN | GS-01 | Intermittent Nonseasonal |
| 214123 | 1 | SI | 5406-UTILITY SYSTEMS OPERATING | WS-12 | Full-time Nonseasonal |
853 rows × 5 columns
These 1293 separation observations do not have coverage within the EMP Dataset, thus, we will remove these observations as out of scope demographic in our analysis. Any attempt in predicting these values will not have enough data to support a significant response.
OPMDataMerged = OPMDataMerged[OPMDataMerged["IndAvgSalary"].notnull()]
print(len(OPMDataMerged[OPMDataMerged["IndAvgSalary"].isnull()]))
print(len(OPMDataMerged))
0 8170907
# Placeholder Chunks for Data Quality check of salary against GS Grade Level Ranges
We are iterested to see how federal pension plans may impact attrition in this dataset. An interesting attribute to complement Length of service, is Years to Retirement. Utilizing a FERS retirement eligibility baseline of 57 years of age for all observations, and the lower limitation of age level ranges we compute a numeric value for length of retirement.
#Add Column YearsToRetirement
"""
AGELVL,AGELVLT
A,Less than 20
B,20-24
C,25-29
D,30-34
E,35-39
F,40-44
G,45-49
H,50-54
I,55-59
J,60-64
K,65 or more
Z,Unspecified
"""
OPMDataMerged["LowerLimitAge"] = np.where(OPMDataMerged["AGELVL"]=="B", 20,
np.where(OPMDataMerged["AGELVL"]=="C", 25,
np.where(OPMDataMerged["AGELVL"]=="D", 30,
np.where(OPMDataMerged["AGELVL"]=="E", 35,
np.where(OPMDataMerged["AGELVL"]=="F", 40,
np.where(OPMDataMerged["AGELVL"]=="G", 45,
np.where(OPMDataMerged["AGELVL"]=="H", 50,
np.where(OPMDataMerged["AGELVL"]=="I", 55,
np.where(OPMDataMerged["AGELVL"]=="J", 60,
np.where(OPMDataMerged["AGELVL"]=="K", 65,
np.nan
)
)
)
)
)
)
)
)
)
)
retAge = 57
OPMDataMerged["YearsToRetirement"] = np.where(OPMDataMerged["AGELVL"]=="B", retAge-20,
np.where(OPMDataMerged["AGELVL"]=="C", retAge-25,
np.where(OPMDataMerged["AGELVL"]=="D", retAge-30,
np.where(OPMDataMerged["AGELVL"]=="E", retAge-35,
np.where(OPMDataMerged["AGELVL"]=="F", retAge-40,
np.where(OPMDataMerged["AGELVL"]=="G", retAge-45,
np.where(OPMDataMerged["AGELVL"]=="H", retAge-50,
np.where(OPMDataMerged["AGELVL"]=="I", retAge-55,
np.where(OPMDataMerged["AGELVL"]=="J", retAge-60,
np.where(OPMDataMerged["AGELVL"]=="K", retAge-65,
np.nan
)
)
)
)
)
)
)
)
)
)
print("Null Values for LowerLimitAge: " + str(len(OPMDataMerged[OPMDataMerged["LowerLimitAge"].isnull()])))
print("Null Values for YearsToRetirement: " + str(len(OPMDataMerged[OPMDataMerged["YearsToRetirement"].isnull()])))
display(OPMDataMerged.head())
display(OPMDataMerged.tail())
Null Values for LowerLimitAge: 0 Null Values for YearsToRetirement: 0
| AGYSUB | SEP | DATECODE | AGELVL | GENDER | GSEGRD | LOSLVL | LOC | OCC | PATCO | PPGRD | SALLVL | TOA | WORKSCH | COUNT | SALARY | LOS | AGYTYP | AGYTYPT | AGY | AGYT | AGYSUBT | QTR | AGELVLT | LOSLVLT | LOCTYP | LOCTYPT | LOCT | OCCTYP | OCCTYPT | OCCFAM | OCCFAMT | OCCT | PATCOT | PPTYP | PPTYPT | PPGROUP | PPGROUPT | PAYPLAN | PAYPLANT | SALLVLT | TOATYP | TOATYPT | TOAT | WSTYP | WSTYPT | WORKSCHT | SEPCount_EFDATE_OCC | SEPCount_EFDATE_LOC | IndAvgSalary | SalaryOverUnderIndAvg | LowerLimitAge | YearsToRetirement | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | AA00 | SC | 201507 | C | M | 11 | A | 11 | 0905 | 1 | GS-11 | F | 40 | F | 1.0 | 63722.0 | 0.8 | 4 | Small Independent Agencies (less than 100 empl... | AA | AA-ADMINISTRATIVE CONFERENCE OF THE UNITED STATES | AA00-ADMINISTRATIVE CONFERENCE OF THE UNITED S... | 4 | 25-29 | Less than 1 year | 1 | United States | 11-DISTRICT OF COLUMBIA | 1 | White Collar | 09 | 09xx-LEGAL AND KINDRED | 0905-GENERAL ATTORNEY | Professional | 1 | General Schedule and Equivalently Graded (GSEG... | 11 | Standard GSEG Pay Plans | GS | GS-GENERAL SCHEDULE | $60,000 - $69,999 | 2 | Non-permanent | 40-Excepted Service - Schedule A | 1 | Full-time | Full-time Nonseasonal | 205.0 | 1319 | 64540.593830 | -818.593830 | 25.0 | 32.0 |
| 1 | AA00 | SC | 201506 | D | F | 15 | C | 11 | 0905 | 1 | GS-15 | L | 30 | F | 1.0 | 126245.0 | 4.8 | 4 | Small Independent Agencies (less than 100 empl... | AA | AA-ADMINISTRATIVE CONFERENCE OF THE UNITED STATES | AA00-ADMINISTRATIVE CONFERENCE OF THE UNITED S... | 3 | 30-34 | 3 - 4 years | 1 | United States | 11-DISTRICT OF COLUMBIA | 1 | White Collar | 09 | 09xx-LEGAL AND KINDRED | 0905-GENERAL ATTORNEY | Professional | 1 | General Schedule and Equivalently Graded (GSEG... | 11 | Standard GSEG Pay Plans | GS | GS-GENERAL SCHEDULE | $120,000 - $129,999 | 1 | Permanent | 30-Excepted Service - Schedule A | 1 | Full-time | Full-time Nonseasonal | 207.0 | 1132 | 149864.298504 | -23619.298504 | 30.0 | 27.0 |
| 2 | AF** | SA | 201503 | H | M | 11 | C | 48 | 2210 | 2 | GS-11 | F | 10 | F | 1.0 | 66585.0 | 4.9 | 1 | Cabinet Level Agencies | AF | AF-DEPARTMENT OF THE AIR FORCE | AF**-INVALID | 2 | 50-54 | 3 - 4 years | 1 | United States | 48-TEXAS | 1 | White Collar | 22 | 22xx-INFORMATION TECHNOLOGY | 2210-INFORMATION TECHNOLOGY MANAGEMENT | Administrative | 1 | General Schedule and Equivalently Graded (GSEG... | 11 | Standard GSEG Pay Plans | GS | GS-GENERAL SCHEDULE | $60,000 - $69,999 | 1 | Permanent | 10-Competitive Service - Career | 1 | Full-time | Full-time Nonseasonal | 439.0 | 1087 | 71530.963755 | -4945.963755 | 50.0 | 7.0 |
| 3 | AF02 | SD | 201506 | I | M | 15 | J | 35 | 0301 | 2 | GS-15 | O | 10 | F | 1.0 | 156737.0 | 39.8 | 1 | Cabinet Level Agencies | AF | AF-DEPARTMENT OF THE AIR FORCE | AF02-AIR FORCE INSPECTION AGENCY (FO) | 3 | 55-59 | 35 years or more | 1 | United States | 35-NEW MEXICO | 1 | White Collar | 03 | 03xx-GENERAL ADMIN, CLERICAL, & OFFICE SVCS | 0301-MISCELLANEOUS ADMINISTRATION AND PROGRAM | Administrative | 1 | General Schedule and Equivalently Graded (GSEG... | 11 | Standard GSEG Pay Plans | GS | GS-GENERAL SCHEDULE | $150,000 - $159,999 | 1 | Permanent | 10-Competitive Service - Career | 1 | Full-time | Full-time Nonseasonal | 670.0 | 265 | 146735.220304 | 10001.779696 | 55.0 | 2.0 |
| 4 | AF03 | SC | 201509 | H | M | 13 | B | 06 | 0301 | 2 | GS-13 | I | 15 | F | 1.0 | 92973.0 | 1.0 | 1 | Cabinet Level Agencies | AF | AF-DEPARTMENT OF THE AIR FORCE | AF03-AIR FORCE OPERATIONAL TEST AND EVALUATION... | 4 | 50-54 | 1 - 2 years | 1 | United States | 06-CALIFORNIA | 1 | White Collar | 03 | 03xx-GENERAL ADMIN, CLERICAL, & OFFICE SVCS | 0301-MISCELLANEOUS ADMINISTRATION AND PROGRAM | Administrative | 1 | General Schedule and Equivalently Graded (GSEG... | 11 | Standard GSEG Pay Plans | GS | GS-GENERAL SCHEDULE | $90,000 - $99,999 | 1 | Permanent | 15-Competitive Service - Career-Conditional | 1 | Full-time | Full-time Nonseasonal | 721.0 | 1853 | 101641.124025 | -8668.124025 | 50.0 | 7.0 |
| AGYSUB | SEP | DATECODE | AGELVL | GENDER | GSEGRD | LOSLVL | LOC | OCC | PATCO | PPGRD | SALLVL | TOA | WORKSCH | COUNT | SALARY | LOS | AGYTYP | AGYTYPT | AGY | AGYT | AGYSUBT | QTR | AGELVLT | LOSLVLT | LOCTYP | LOCTYPT | LOCT | OCCTYP | OCCTYPT | OCCFAM | OCCFAMT | OCCT | PATCOT | PPTYP | PPTYPT | PPGROUP | PPGROUPT | PAYPLAN | PAYPLANT | SALLVLT | TOATYP | TOATYPT | TOAT | WSTYP | WSTYPT | WORKSCHT | SEPCount_EFDATE_OCC | SEPCount_EFDATE_LOC | IndAvgSalary | SalaryOverUnderIndAvg | LowerLimitAge | YearsToRetirement | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 8223188 | ZU00 | NS | 201509 | D | NaN | NaN | C | 11 | 0301 | 2 | AD-00 | G | 48 | F | NaN | 76377.0 | 4.8 | 4 | Small Independent Agencies (less than 100 empl... | ZU | ZU-DWIGHT D. EISENHOWER MEMORIAL COMMISSION | ZU00-DWIGHT D. EISENHOWER MEMORIAL COMMISSION | 4 | 30-34 | 3 - 4 years | 1 | United States | 11-DISTRICT OF COLUMBIA | 1 | White Collar | 03 | 03xx-GENERAL ADMIN, CLERICAL, & OFFICE SVCS | 0301-MISCELLANEOUS ADMINISTRATION AND PROGRAM | Administrative | 3 | Other White Collar Pay Plans | 31 | Governmentwide or Multi-Agency Plans | AD | AD-ADMINISTRATIVELY DETERMINED RATES, NOT ELSE... | $70,000 - $79,999 | 2 | Non-permanent | 48-Excepted Service - Other | 1 | Full-time | Full-time Nonseasonal | 721.0 | 1391 | 115840.182250 | -39463.182250 | 30.0 | 27.0 |
| 8223189 | ZU00 | NS | 201509 | K | NaN | NaN | D | 11 | 0301 | 2 | AD-00 | M | 48 | F | NaN | 139517.0 | 7.0 | 4 | Small Independent Agencies (less than 100 empl... | ZU | ZU-DWIGHT D. EISENHOWER MEMORIAL COMMISSION | ZU00-DWIGHT D. EISENHOWER MEMORIAL COMMISSION | 4 | 65 or more | 5 - 9 years | 1 | United States | 11-DISTRICT OF COLUMBIA | 1 | White Collar | 03 | 03xx-GENERAL ADMIN, CLERICAL, & OFFICE SVCS | 0301-MISCELLANEOUS ADMINISTRATION AND PROGRAM | Administrative | 3 | Other White Collar Pay Plans | 31 | Governmentwide or Multi-Agency Plans | AD | AD-ADMINISTRATIVELY DETERMINED RATES, NOT ELSE... | $130,000 - $139,999 | 2 | Non-permanent | 48-Excepted Service - Other | 1 | Full-time | Full-time Nonseasonal | 721.0 | 1391 | 115840.182250 | 23676.817750 | 65.0 | -8.0 |
| 8223190 | ZU00 | NS | 201509 | K | NaN | NaN | D | 11 | 0301 | 2 | AD-00 | O | 48 | F | NaN | 158671.0 | 7.0 | 4 | Small Independent Agencies (less than 100 empl... | ZU | ZU-DWIGHT D. EISENHOWER MEMORIAL COMMISSION | ZU00-DWIGHT D. EISENHOWER MEMORIAL COMMISSION | 4 | 65 or more | 5 - 9 years | 1 | United States | 11-DISTRICT OF COLUMBIA | 1 | White Collar | 03 | 03xx-GENERAL ADMIN, CLERICAL, & OFFICE SVCS | 0301-MISCELLANEOUS ADMINISTRATION AND PROGRAM | Administrative | 3 | Other White Collar Pay Plans | 31 | Governmentwide or Multi-Agency Plans | AD | AD-ADMINISTRATIVELY DETERMINED RATES, NOT ELSE... | $150,000 - $159,999 | 2 | Non-permanent | 48-Excepted Service - Other | 1 | Full-time | Full-time Nonseasonal | 721.0 | 1391 | 115840.182250 | 42830.817750 | 65.0 | -8.0 |
| 8223191 | ZU00 | NS | 201509 | B | NaN | NaN | B | 11 | 0301 | 2 | AD-00 | C | 48 | F | NaN | 36244.0 | 1.6 | 4 | Small Independent Agencies (less than 100 empl... | ZU | ZU-DWIGHT D. EISENHOWER MEMORIAL COMMISSION | ZU00-DWIGHT D. EISENHOWER MEMORIAL COMMISSION | 4 | 20-24 | 1 - 2 years | 1 | United States | 11-DISTRICT OF COLUMBIA | 1 | White Collar | 03 | 03xx-GENERAL ADMIN, CLERICAL, & OFFICE SVCS | 0301-MISCELLANEOUS ADMINISTRATION AND PROGRAM | Administrative | 3 | Other White Collar Pay Plans | 31 | Governmentwide or Multi-Agency Plans | AD | AD-ADMINISTRATIVELY DETERMINED RATES, NOT ELSE... | $30,000 - $39,999 | 2 | Non-permanent | 48-Excepted Service - Other | 1 | Full-time | Full-time Nonseasonal | 721.0 | 1391 | 115840.182250 | -79596.182250 | 20.0 | 37.0 |
| 8223192 | ZU00 | NS | 201509 | E | NaN | NaN | D | 11 | 0505 | 2 | AD-00 | I | 48 | F | NaN | 99288.0 | 5.0 | 4 | Small Independent Agencies (less than 100 empl... | ZU | ZU-DWIGHT D. EISENHOWER MEMORIAL COMMISSION | ZU00-DWIGHT D. EISENHOWER MEMORIAL COMMISSION | 4 | 35-39 | 5 - 9 years | 1 | United States | 11-DISTRICT OF COLUMBIA | 1 | White Collar | 05 | 05xx-ACCOUNTING AND BUDGET | 0505-FINANCIAL MANAGEMENT | Administrative | 3 | Other White Collar Pay Plans | 31 | Governmentwide or Multi-Agency Plans | AD | AD-ADMINISTRATIVELY DETERMINED RATES, NOT ELSE... | $90,000 - $99,999 | 2 | Non-permanent | 48-Excepted Service - Other | 1 | Full-time | Full-time Nonseasonal | 7.0 | 1391 | 148382.833333 | -49094.833333 | 35.0 | 22.0 |
In addition to the OPM data, we merge 10 attributes from the Bureau of Labor Statistics (BLS). Data is sourced from Federal Government industry codes across all regions. Although assumed to be highly correlated, we source both Level (Total number) and Rate (Percentage of Level to total employment and / or job openings) for the following statistics: 1) Job Openings, 2) Layoffs, 3) Quits, 4) Total Separations, and 5) Other Separations. While Rate paints an aggregated, holistic picture for job market trends, Level provides a raw count for total separations alone. Both these statistics were captured by a monthly aggregate and merged to the OPM data by their respective months.
%%time
def bls(series, start, end):
headers = {'Content-type': 'application/json'}
sID = []
for i in range(0,len(series)):
sID.append(series[i][0])
data = json.dumps({"seriesid": sID,
"startyear":start,
"endyear":end,
"catalog":False,
"calculations":False,
"annualaverage":False,
"registrationkey":"7a89c8d7979349fba8914b8be16a1646"})
p = requests.post('https://api.bls.gov/publicAPI/v2/timeseries/data/', data=data, headers=headers)
json_data = json.loads(p.text)
bls = []
for series in json_data['Results']['series']:
#x=prettytable.PrettyTable(["series id","year","period","value","footnotes"])
result = pd.DataFrame(columns=["series id","year","period","value","footnotes"])
seriesId = series['seriesID']
for item in series['data']:
year = item['year']
period = item['period']
value = item['value']
footnotes=""
for footnote in item['footnotes']:
if footnote:
footnotes = footnotes + footnote['text'] + ','
if 'M01' <= period <= 'M12':
#x.add_row([seriesId,year,period,value,footnotes[0:-1]])
y = pd.DataFrame({"series id" : seriesId,
"year" : year,
"period" : period,
"value" : value,
"footnotes" : footnotes}, index = [0])
result = result.append(y, ignore_index = True)
bls.append(result)
return(bls)
CPU times: user 2 µs, sys: 0 ns, total: 2 µs Wall time: 7.15 µs
%%time
seriesList = [
['JTU91000000JOL','BLS_FEDERAL_JobOpenings_Level'],
['JTU91000000LDL','BLS_FEDERAL_Layoffs_Level'],
['JTU91000000OSL','BLS_FEDERAL_OtherSep_Level'],
['JTU91000000QUL','BLS_FEDERAL_Quits_Level'],
['JTU91000000TSL','BLS_FEDERAL_TotalSep_Level'],
['JTU91000000JOR','BLS_FEDERAL_JobOpenings_Rate'],
['JTU91000000LDR','BLS_FEDERAL_Layoffs_Rate'],
['JTU91000000OSR','BLS_FEDERAL_OtherSep_Rate'],
['JTU91000000QUR','BLS_FEDERAL_Quits_Rate'],
['JTU91000000TSR','BLS_FEDERAL_TotalSep_Rate']
]
# Pull job openings and labor turnover data
JTL = bls(seriesList, "2014", "2015")
seriesList = pd.DataFrame(seriesList, columns = ["series id","sName"])
##We need to replace these with actual Descriptor Column Names
for i in range(0,len(seriesList)):
JTL[i] = JTL[i].merge(seriesList, on = "series id", how = 'inner')
if len(JTL[i]) >0:
name = JTL[i]["sName"].drop_duplicates().values[0]
else:
name = str(i)
JTL[i][name] = JTL[i]["value"].apply(pd.to_numeric)
JTL[i]["DATECODE"] = JTL[i]["year"] + JTL[i]["period"].str[-2:]
del JTL[i]["value"]
del JTL[i]["year"]
del JTL[i]["period"]
del JTL[i]["series id"]
del JTL[i]["footnotes"]
del JTL[i]["sName"]
OPMDataMerged = OPMDataMerged.merge(JTL[i], on = "DATECODE", how = 'left')
display(JTL[i].head())
| BLS_FEDERAL_OtherSep_Rate | DATECODE | |
|---|---|---|
| 0 | 0.4 | 201512 |
| 1 | 0.4 | 201511 |
| 2 | 0.4 | 201510 |
| 3 | 0.4 | 201509 |
| 4 | 0.5 | 201508 |
| BLS_FEDERAL_Quits_Rate | DATECODE | |
|---|---|---|
| 0 | 0.4 | 201512 |
| 1 | 0.4 | 201511 |
| 2 | 0.6 | 201510 |
| 3 | 0.5 | 201509 |
| 4 | 0.6 | 201508 |
| BLS_FEDERAL_TotalSep_Level | DATECODE | |
|---|---|---|
| 0 | 37 | 201512 |
| 1 | 35 | 201511 |
| 2 | 45 | 201510 |
| 3 | 38 | 201509 |
| 4 | 41 | 201508 |
| BLS_FEDERAL_JobOpenings_Rate | DATECODE | |
|---|---|---|
| 0 | 2.9 | 201512 |
| 1 | 2.6 | 201511 |
| 2 | 2.4 | 201510 |
| 3 | 1.9 | 201509 |
| 4 | 2.3 | 201508 |
| BLS_FEDERAL_OtherSep_Level | DATECODE | |
|---|---|---|
| 0 | 12 | 201512 |
| 1 | 10 | 201511 |
| 2 | 12 | 201510 |
| 3 | 12 | 201509 |
| 4 | 14 | 201508 |
| BLS_FEDERAL_Quits_Level | DATECODE | |
|---|---|---|
| 0 | 11 | 201512 |
| 1 | 10 | 201511 |
| 2 | 16 | 201510 |
| 3 | 14 | 201509 |
| 4 | 17 | 201508 |
| BLS_FEDERAL_JobOpenings_Level | DATECODE | |
|---|---|---|
| 0 | 83 | 201512 |
| 1 | 73 | 201511 |
| 2 | 68 | 201510 |
| 3 | 55 | 201509 |
| 4 | 67 | 201508 |
| BLS_FEDERAL_Layoffs_Rate | DATECODE | |
|---|---|---|
| 0 | 0.5 | 201512 |
| 1 | 0.6 | 201511 |
| 2 | 0.6 | 201510 |
| 3 | 0.4 | 201509 |
| 4 | 0.3 | 201508 |
| BLS_FEDERAL_Layoffs_Level | DATECODE | |
|---|---|---|
| 0 | 15 | 201512 |
| 1 | 15 | 201511 |
| 2 | 18 | 201510 |
| 3 | 12 | 201509 |
| 4 | 10 | 201508 |
| BLS_FEDERAL_TotalSep_Rate | DATECODE | |
|---|---|---|
| 0 | 1.3 | 201512 |
| 1 | 1.3 | 201511 |
| 2 | 1.6 | 201510 |
| 3 | 1.4 | 201509 |
| 4 | 1.5 | 201508 |
CPU times: user 37.4 s, sys: 10.1 s, total: 47.4 s Wall time: 47.9 s
display(OPMDataMerged.head())
display(OPMDataMerged.tail())
| AGYSUB | SEP | DATECODE | AGELVL | GENDER | GSEGRD | LOSLVL | LOC | OCC | PATCO | PPGRD | SALLVL | TOA | WORKSCH | COUNT | SALARY | LOS | AGYTYP | AGYTYPT | AGY | AGYT | AGYSUBT | QTR | AGELVLT | LOSLVLT | LOCTYP | LOCTYPT | LOCT | OCCTYP | OCCTYPT | OCCFAM | OCCFAMT | OCCT | PATCOT | PPTYP | PPTYPT | PPGROUP | PPGROUPT | PAYPLAN | PAYPLANT | SALLVLT | TOATYP | TOATYPT | TOAT | WSTYP | WSTYPT | WORKSCHT | SEPCount_EFDATE_OCC | SEPCount_EFDATE_LOC | IndAvgSalary | SalaryOverUnderIndAvg | LowerLimitAge | YearsToRetirement | BLS_FEDERAL_OtherSep_Rate | BLS_FEDERAL_Quits_Rate | BLS_FEDERAL_TotalSep_Level | BLS_FEDERAL_JobOpenings_Rate | BLS_FEDERAL_OtherSep_Level | BLS_FEDERAL_Quits_Level | BLS_FEDERAL_JobOpenings_Level | BLS_FEDERAL_Layoffs_Rate | BLS_FEDERAL_Layoffs_Level | BLS_FEDERAL_TotalSep_Rate | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | AA00 | SC | 201507 | C | M | 11 | A | 11 | 0905 | 1 | GS-11 | F | 40 | F | 1.0 | 63722.0 | 0.8 | 4 | Small Independent Agencies (less than 100 empl... | AA | AA-ADMINISTRATIVE CONFERENCE OF THE UNITED STATES | AA00-ADMINISTRATIVE CONFERENCE OF THE UNITED S... | 4 | 25-29 | Less than 1 year | 1 | United States | 11-DISTRICT OF COLUMBIA | 1 | White Collar | 09 | 09xx-LEGAL AND KINDRED | 0905-GENERAL ATTORNEY | Professional | 1 | General Schedule and Equivalently Graded (GSEG... | 11 | Standard GSEG Pay Plans | GS | GS-GENERAL SCHEDULE | $60,000 - $69,999 | 2 | Non-permanent | 40-Excepted Service - Schedule A | 1 | Full-time | Full-time Nonseasonal | 205.0 | 1319 | 64540.593830 | -818.593830 | 25.0 | 32.0 | 0.4 | 0.5 | 34 | 2.6 | 11 | 13 | 74 | 0.4 | 10 | 1.2 |
| 1 | AA00 | SC | 201506 | D | F | 15 | C | 11 | 0905 | 1 | GS-15 | L | 30 | F | 1.0 | 126245.0 | 4.8 | 4 | Small Independent Agencies (less than 100 empl... | AA | AA-ADMINISTRATIVE CONFERENCE OF THE UNITED STATES | AA00-ADMINISTRATIVE CONFERENCE OF THE UNITED S... | 3 | 30-34 | 3 - 4 years | 1 | United States | 11-DISTRICT OF COLUMBIA | 1 | White Collar | 09 | 09xx-LEGAL AND KINDRED | 0905-GENERAL ATTORNEY | Professional | 1 | General Schedule and Equivalently Graded (GSEG... | 11 | Standard GSEG Pay Plans | GS | GS-GENERAL SCHEDULE | $120,000 - $129,999 | 1 | Permanent | 30-Excepted Service - Schedule A | 1 | Full-time | Full-time Nonseasonal | 207.0 | 1132 | 149864.298504 | -23619.298504 | 30.0 | 27.0 | 0.4 | 0.5 | 34 | 2.3 | 12 | 13 | 65 | 0.4 | 10 | 1.2 |
| 2 | AF** | SA | 201503 | H | M | 11 | C | 48 | 2210 | 2 | GS-11 | F | 10 | F | 1.0 | 66585.0 | 4.9 | 1 | Cabinet Level Agencies | AF | AF-DEPARTMENT OF THE AIR FORCE | AF**-INVALID | 2 | 50-54 | 3 - 4 years | 1 | United States | 48-TEXAS | 1 | White Collar | 22 | 22xx-INFORMATION TECHNOLOGY | 2210-INFORMATION TECHNOLOGY MANAGEMENT | Administrative | 1 | General Schedule and Equivalently Graded (GSEG... | 11 | Standard GSEG Pay Plans | GS | GS-GENERAL SCHEDULE | $60,000 - $69,999 | 1 | Permanent | 10-Competitive Service - Career | 1 | Full-time | Full-time Nonseasonal | 439.0 | 1087 | 71530.963755 | -4945.963755 | 50.0 | 7.0 | 0.3 | 0.4 | 31 | 3.0 | 9 | 10 | 86 | 0.5 | 12 | 1.1 |
| 3 | AF02 | SD | 201506 | I | M | 15 | J | 35 | 0301 | 2 | GS-15 | O | 10 | F | 1.0 | 156737.0 | 39.8 | 1 | Cabinet Level Agencies | AF | AF-DEPARTMENT OF THE AIR FORCE | AF02-AIR FORCE INSPECTION AGENCY (FO) | 3 | 55-59 | 35 years or more | 1 | United States | 35-NEW MEXICO | 1 | White Collar | 03 | 03xx-GENERAL ADMIN, CLERICAL, & OFFICE SVCS | 0301-MISCELLANEOUS ADMINISTRATION AND PROGRAM | Administrative | 1 | General Schedule and Equivalently Graded (GSEG... | 11 | Standard GSEG Pay Plans | GS | GS-GENERAL SCHEDULE | $150,000 - $159,999 | 1 | Permanent | 10-Competitive Service - Career | 1 | Full-time | Full-time Nonseasonal | 670.0 | 265 | 146735.220304 | 10001.779696 | 55.0 | 2.0 | 0.4 | 0.5 | 34 | 2.3 | 12 | 13 | 65 | 0.4 | 10 | 1.2 |
| 4 | AF03 | SC | 201509 | H | M | 13 | B | 06 | 0301 | 2 | GS-13 | I | 15 | F | 1.0 | 92973.0 | 1.0 | 1 | Cabinet Level Agencies | AF | AF-DEPARTMENT OF THE AIR FORCE | AF03-AIR FORCE OPERATIONAL TEST AND EVALUATION... | 4 | 50-54 | 1 - 2 years | 1 | United States | 06-CALIFORNIA | 1 | White Collar | 03 | 03xx-GENERAL ADMIN, CLERICAL, & OFFICE SVCS | 0301-MISCELLANEOUS ADMINISTRATION AND PROGRAM | Administrative | 1 | General Schedule and Equivalently Graded (GSEG... | 11 | Standard GSEG Pay Plans | GS | GS-GENERAL SCHEDULE | $90,000 - $99,999 | 1 | Permanent | 15-Competitive Service - Career-Conditional | 1 | Full-time | Full-time Nonseasonal | 721.0 | 1853 | 101641.124025 | -8668.124025 | 50.0 | 7.0 | 0.4 | 0.5 | 38 | 1.9 | 12 | 14 | 55 | 0.4 | 12 | 1.4 |
| AGYSUB | SEP | DATECODE | AGELVL | GENDER | GSEGRD | LOSLVL | LOC | OCC | PATCO | PPGRD | SALLVL | TOA | WORKSCH | COUNT | SALARY | LOS | AGYTYP | AGYTYPT | AGY | AGYT | AGYSUBT | QTR | AGELVLT | LOSLVLT | LOCTYP | LOCTYPT | LOCT | OCCTYP | OCCTYPT | OCCFAM | OCCFAMT | OCCT | PATCOT | PPTYP | PPTYPT | PPGROUP | PPGROUPT | PAYPLAN | PAYPLANT | SALLVLT | TOATYP | TOATYPT | TOAT | WSTYP | WSTYPT | WORKSCHT | SEPCount_EFDATE_OCC | SEPCount_EFDATE_LOC | IndAvgSalary | SalaryOverUnderIndAvg | LowerLimitAge | YearsToRetirement | BLS_FEDERAL_OtherSep_Rate | BLS_FEDERAL_Quits_Rate | BLS_FEDERAL_TotalSep_Level | BLS_FEDERAL_JobOpenings_Rate | BLS_FEDERAL_OtherSep_Level | BLS_FEDERAL_Quits_Level | BLS_FEDERAL_JobOpenings_Level | BLS_FEDERAL_Layoffs_Rate | BLS_FEDERAL_Layoffs_Level | BLS_FEDERAL_TotalSep_Rate | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 8170902 | ZU00 | NS | 201509 | D | NaN | NaN | C | 11 | 0301 | 2 | AD-00 | G | 48 | F | NaN | 76377.0 | 4.8 | 4 | Small Independent Agencies (less than 100 empl... | ZU | ZU-DWIGHT D. EISENHOWER MEMORIAL COMMISSION | ZU00-DWIGHT D. EISENHOWER MEMORIAL COMMISSION | 4 | 30-34 | 3 - 4 years | 1 | United States | 11-DISTRICT OF COLUMBIA | 1 | White Collar | 03 | 03xx-GENERAL ADMIN, CLERICAL, & OFFICE SVCS | 0301-MISCELLANEOUS ADMINISTRATION AND PROGRAM | Administrative | 3 | Other White Collar Pay Plans | 31 | Governmentwide or Multi-Agency Plans | AD | AD-ADMINISTRATIVELY DETERMINED RATES, NOT ELSE... | $70,000 - $79,999 | 2 | Non-permanent | 48-Excepted Service - Other | 1 | Full-time | Full-time Nonseasonal | 721.0 | 1391 | 115840.182250 | -39463.182250 | 30.0 | 27.0 | 0.4 | 0.5 | 38 | 1.9 | 12 | 14 | 55 | 0.4 | 12 | 1.4 |
| 8170903 | ZU00 | NS | 201509 | K | NaN | NaN | D | 11 | 0301 | 2 | AD-00 | M | 48 | F | NaN | 139517.0 | 7.0 | 4 | Small Independent Agencies (less than 100 empl... | ZU | ZU-DWIGHT D. EISENHOWER MEMORIAL COMMISSION | ZU00-DWIGHT D. EISENHOWER MEMORIAL COMMISSION | 4 | 65 or more | 5 - 9 years | 1 | United States | 11-DISTRICT OF COLUMBIA | 1 | White Collar | 03 | 03xx-GENERAL ADMIN, CLERICAL, & OFFICE SVCS | 0301-MISCELLANEOUS ADMINISTRATION AND PROGRAM | Administrative | 3 | Other White Collar Pay Plans | 31 | Governmentwide or Multi-Agency Plans | AD | AD-ADMINISTRATIVELY DETERMINED RATES, NOT ELSE... | $130,000 - $139,999 | 2 | Non-permanent | 48-Excepted Service - Other | 1 | Full-time | Full-time Nonseasonal | 721.0 | 1391 | 115840.182250 | 23676.817750 | 65.0 | -8.0 | 0.4 | 0.5 | 38 | 1.9 | 12 | 14 | 55 | 0.4 | 12 | 1.4 |
| 8170904 | ZU00 | NS | 201509 | K | NaN | NaN | D | 11 | 0301 | 2 | AD-00 | O | 48 | F | NaN | 158671.0 | 7.0 | 4 | Small Independent Agencies (less than 100 empl... | ZU | ZU-DWIGHT D. EISENHOWER MEMORIAL COMMISSION | ZU00-DWIGHT D. EISENHOWER MEMORIAL COMMISSION | 4 | 65 or more | 5 - 9 years | 1 | United States | 11-DISTRICT OF COLUMBIA | 1 | White Collar | 03 | 03xx-GENERAL ADMIN, CLERICAL, & OFFICE SVCS | 0301-MISCELLANEOUS ADMINISTRATION AND PROGRAM | Administrative | 3 | Other White Collar Pay Plans | 31 | Governmentwide or Multi-Agency Plans | AD | AD-ADMINISTRATIVELY DETERMINED RATES, NOT ELSE... | $150,000 - $159,999 | 2 | Non-permanent | 48-Excepted Service - Other | 1 | Full-time | Full-time Nonseasonal | 721.0 | 1391 | 115840.182250 | 42830.817750 | 65.0 | -8.0 | 0.4 | 0.5 | 38 | 1.9 | 12 | 14 | 55 | 0.4 | 12 | 1.4 |
| 8170905 | ZU00 | NS | 201509 | B | NaN | NaN | B | 11 | 0301 | 2 | AD-00 | C | 48 | F | NaN | 36244.0 | 1.6 | 4 | Small Independent Agencies (less than 100 empl... | ZU | ZU-DWIGHT D. EISENHOWER MEMORIAL COMMISSION | ZU00-DWIGHT D. EISENHOWER MEMORIAL COMMISSION | 4 | 20-24 | 1 - 2 years | 1 | United States | 11-DISTRICT OF COLUMBIA | 1 | White Collar | 03 | 03xx-GENERAL ADMIN, CLERICAL, & OFFICE SVCS | 0301-MISCELLANEOUS ADMINISTRATION AND PROGRAM | Administrative | 3 | Other White Collar Pay Plans | 31 | Governmentwide or Multi-Agency Plans | AD | AD-ADMINISTRATIVELY DETERMINED RATES, NOT ELSE... | $30,000 - $39,999 | 2 | Non-permanent | 48-Excepted Service - Other | 1 | Full-time | Full-time Nonseasonal | 721.0 | 1391 | 115840.182250 | -79596.182250 | 20.0 | 37.0 | 0.4 | 0.5 | 38 | 1.9 | 12 | 14 | 55 | 0.4 | 12 | 1.4 |
| 8170906 | ZU00 | NS | 201509 | E | NaN | NaN | D | 11 | 0505 | 2 | AD-00 | I | 48 | F | NaN | 99288.0 | 5.0 | 4 | Small Independent Agencies (less than 100 empl... | ZU | ZU-DWIGHT D. EISENHOWER MEMORIAL COMMISSION | ZU00-DWIGHT D. EISENHOWER MEMORIAL COMMISSION | 4 | 35-39 | 5 - 9 years | 1 | United States | 11-DISTRICT OF COLUMBIA | 1 | White Collar | 05 | 05xx-ACCOUNTING AND BUDGET | 0505-FINANCIAL MANAGEMENT | Administrative | 3 | Other White Collar Pay Plans | 31 | Governmentwide or Multi-Agency Plans | AD | AD-ADMINISTRATIVELY DETERMINED RATES, NOT ELSE... | $90,000 - $99,999 | 2 | Non-permanent | 48-Excepted Service - Other | 1 | Full-time | Full-time Nonseasonal | 7.0 | 1391 | 148382.833333 | -49094.833333 | 35.0 | 22.0 | 0.4 | 0.5 | 38 | 1.9 | 12 | 14 | 55 | 0.4 | 12 | 1.4 |
display(pd.DataFrame({'StratCount' : OPMDataMerged.groupby(["SEP"]).size()}).reset_index())
| SEP | StratCount | |
|---|---|---|
| 0 | NS | 7957918 |
| 1 | SA | 26945 |
| 2 | SB | 333 |
| 3 | SC | 66248 |
| 4 | SD | 56820 |
| 5 | SE | 1260 |
| 6 | SF | 4100 |
| 7 | SG | 1467 |
| 8 | SH | 400 |
| 9 | SI | 9728 |
| 10 | SJ | 42754 |
| 11 | SK | 2892 |
| 12 | SL | 42 |
In terms of data exploration, we first investigate numeric type attributes. Relationships, distributions, and correlation values are reviewed.
A new binary separation attribute is created to indicate whether non-sep or sep for EDA correlation purposes
#%%time
#
#
#cols = list(SampledOPMData.select_dtypes(include=['float64', 'int64']))
#cols.remove('COUNT')
#cols.remove('BLS_FEDERAL_OtherSep_Rate')
#cols.remove('BLS_FEDERAL_Quits_Rate')
#cols.remove('BLS_FEDERAL_TotalSep_Level')
#cols.remove('BLS_FEDERAL_JobOpenings_Rate')
#cols.remove('BLS_FEDERAL_OtherSep_Level')
#cols.remove('BLS_FEDERAL_Quits_Level')
#cols.remove('BLS_FEDERAL_JobOpenings_Level')
#cols.remove('BLS_FEDERAL_Layoffs_Rate')
#cols.remove('BLS_FEDERAL_Layoffs_Level')
#cols.remove('BLS_FEDERAL_TotalSep_Rate')
#cols.append('SEP')
#display(cols)
#
#plotNumeric = SampledOPMData[cols]
#
## Create binary separation attribute for EDA correlation review
##plotNumeric["SEP_bin"] = plotNumeric.SEP.replace("NS", 1)
##plotNumeric.loc[plotNumeric['SEP_bin'] != 1, 'SEP_bin'] = 0
##plotNumeric.SEP_bin = plotNumeric.SEP_bin.apply(pd.to_numeric)
#AttSplit = pd.get_dummies(plotNumeric['SEP'],prefix='SEP')
#display(AttSplit.head())
#plotNumeric = pd.concat((plotNumeric,AttSplit),axis=1) # add back into the dataframe
#
#display(plotNumeric.head())
#print("plotNumeric has {0} Records".format(len(plotNumeric)))
##print(plotNumeric.SEP_bin.dtype)
%%time
cols = list(OPMDataMerged.select_dtypes(include=['float64', 'int64']))
cols.remove('COUNT')
cols.remove('BLS_FEDERAL_OtherSep_Rate')
cols.remove('BLS_FEDERAL_Quits_Rate')
cols.remove('BLS_FEDERAL_TotalSep_Level')
cols.remove('BLS_FEDERAL_JobOpenings_Rate')
cols.remove('BLS_FEDERAL_OtherSep_Level')
cols.remove('BLS_FEDERAL_Quits_Level')
cols.remove('BLS_FEDERAL_JobOpenings_Level')
cols.remove('BLS_FEDERAL_Layoffs_Rate')
cols.remove('BLS_FEDERAL_Layoffs_Level')
cols.remove('BLS_FEDERAL_TotalSep_Rate')
cols.append('SEP')
display(cols)
plotNumeric = OPMDataMerged[cols]
# Create binary separation attribute for EDA correlation review
#plotNumeric["SEP_bin"] = plotNumeric.SEP.replace("NS", 1)
#plotNumeric.loc[plotNumeric['SEP_bin'] != 1, 'SEP_bin'] = 0
#plotNumeric.SEP_bin = plotNumeric.SEP_bin.apply(pd.to_numeric)
AttSplit = pd.get_dummies(plotNumeric['SEP'],prefix='SEP')
display(AttSplit.head())
plotNumeric = pd.concat((plotNumeric,AttSplit),axis=1) # add back into the dataframe
display(plotNumeric.head())
print("plotNumeric has {0} Records".format(len(plotNumeric)))
#print(plotNumeric.SEP_bin.dtype)
['SALARY', 'LOS', 'SEPCount_EFDATE_OCC', 'SEPCount_EFDATE_LOC', 'IndAvgSalary', 'SalaryOverUnderIndAvg', 'LowerLimitAge', 'YearsToRetirement', 'SEP']
| SEP_NS | SEP_SA | SEP_SB | SEP_SC | SEP_SD | SEP_SE | SEP_SF | SEP_SG | SEP_SH | SEP_SI | SEP_SJ | SEP_SK | SEP_SL | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
| 1 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
| 2 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
| 3 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
| 4 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
| SALARY | LOS | SEPCount_EFDATE_OCC | SEPCount_EFDATE_LOC | IndAvgSalary | SalaryOverUnderIndAvg | LowerLimitAge | YearsToRetirement | SEP | SEP_NS | SEP_SA | SEP_SB | SEP_SC | SEP_SD | SEP_SE | SEP_SF | SEP_SG | SEP_SH | SEP_SI | SEP_SJ | SEP_SK | SEP_SL | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | 63722.0 | 0.8 | 205.0 | 1319 | 64540.593830 | -818.593830 | 25.0 | 32.0 | SC | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
| 1 | 126245.0 | 4.8 | 207.0 | 1132 | 149864.298504 | -23619.298504 | 30.0 | 27.0 | SC | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
| 2 | 66585.0 | 4.9 | 439.0 | 1087 | 71530.963755 | -4945.963755 | 50.0 | 7.0 | SA | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
| 3 | 156737.0 | 39.8 | 670.0 | 265 | 146735.220304 | 10001.779696 | 55.0 | 2.0 | SD | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
| 4 | 92973.0 | 1.0 | 721.0 | 1853 | 101641.124025 | -8668.124025 | 50.0 | 7.0 | SC | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
plotNumeric has 8170907 Records CPU times: user 1 s, sys: 510 ms, total: 1.51 s Wall time: 1.51 s
#%%time
#
#sns.set(font_scale=1)
#sns.pairplot(plotNumeric.drop(["SEP_NS",
# "SEP_SA",
# "SEP_SB",
# "SEP_SC",
# "SEP_SD",
# "SEP_SE",
# "SEP_SF",
# "SEP_SG",
# "SEP_SH",
# "SEP_SI",
# "SEP_SJ",
# "SEP_SK",
# "SEP_SL"
# ], axis=1), hue = 'SEP', palette="hls", plot_kws={"s": 50})
%%time
# Function modified from https://stackoverflow.com/questions/29530355/plotting-multiple-histograms-in-grid
sns.set()
def draw_histograms(df, variables, n_rows, n_cols):
fig=plt.figure(figsize=(20,20))
for i, var_name in enumerate(variables):
ax=fig.add_subplot(n_rows,n_cols,i+1)
df[var_name].hist(bins=20,ax=ax, color='#58D68D')
ax.set_title(var_name+" Distribution")
fig.tight_layout() # Improves appearance a bit.
plt.show()
draw_histograms(plotNumeric.drop(['SEP',
"SEP_NS",
"SEP_SA",
"SEP_SB",
"SEP_SC",
"SEP_SD",
"SEP_SE",
"SEP_SF",
"SEP_SG",
"SEP_SH",
"SEP_SI",
"SEP_SJ",
"SEP_SK",
"SEP_SL"
], axis=1),
plotNumeric.drop(['SEP',
"SEP_NS",
"SEP_SA",
"SEP_SB",
"SEP_SC",
"SEP_SD",
"SEP_SE",
"SEP_SF",
"SEP_SG",
"SEP_SH",
"SEP_SI",
"SEP_SJ",
"SEP_SK",
"SEP_SL"
], axis=1).columns, 6, 3)
/usr/local/es7/lib/python3.5/site-packages/matplotlib/font_manager.py:1297: UserWarning: findfont: Font family ['sans-serif'] not found. Falling back to DejaVu Sans (prop.get_family(), self.defaultFamily[fontext]))
CPU times: user 4.68 s, sys: 6.2 s, total: 10.9 s Wall time: 4.34 s
%%time
# Inspired by http://seaborn.pydata.org/examples/many_pairwise_correlations.html
#plt.matshow(plotNumeric.corr())
sns.set(style='white')
corr = plotNumeric.drop(['SEP'], axis=1).corr()
# Generate a mask for the upper triangle
mask = np.zeros_like(corr, dtype=np.bool)
mask[np.triu_indices_from(mask, k=1)] = True
# Set up the matplotlib figure
f, ax = plt.subplots(figsize=(20, 20))
# Generate a custom diverging colormap
cmap = sns.diverging_palette(250, 10, as_cmap=True)
# Draw the heatmap with the mask and correct aspect ratio
sns.set(font_scale=0.95)
heatCorr = sns.heatmap(corr, mask=mask, cmap=cmap, vmax=1, vmin=-1,
square=True, annot=True, linewidths=1,
cbar_kws={"shrink": .5}, ax=ax, fmt='.1g')
#heatCorr.
ax.tick_params(labelsize=15)
cax = plt.gcf().axes[-1]
cax.tick_params(labelsize=15)
sns.plt.show()
#sns.heatmap(corr, annot=True, linewidths=0.01, cmap=cmap, ax=ax)
/usr/local/es7/lib/python3.5/site-packages/matplotlib/font_manager.py:1297: UserWarning: findfont: Font family ['sans-serif'] not found. Falling back to DejaVu Sans (prop.get_family(), self.defaultFamily[fontext]))
CPU times: user 8.74 s, sys: 1.27 s, total: 10 s Wall time: 8.97 s
Based on the distribution of attributes identified above, we have decided to take the log transform of several attributes.
%%time
# Log Transform Columns Added
OPMDataMerged["SALARYLog"] = OPMDataMerged["SALARY"].apply(np.log)
#OPMDataMerged["LOSLog"] = (OPMDataMerged["LOS"] + .00001).apply(np.log)
OPMDataMerged["LOSSqrt"] = (OPMDataMerged["LOS"]).apply(np.sqrt)
OPMDataMerged["SEPCount_EFDATE_OCCLog"] = OPMDataMerged["SEPCount_EFDATE_OCC"].apply(np.log)
OPMDataMerged["SEPCount_EFDATE_LOCLog"] = OPMDataMerged["SEPCount_EFDATE_LOC"].apply(np.log)
OPMDataMerged["IndAvgSalaryLog"] = OPMDataMerged["IndAvgSalary"].apply(np.log)
CPU times: user 1.05 s, sys: 116 ms, total: 1.17 s Wall time: 1.17 s
We next review categorical data to improve our understanding of factor levels.
#%%time
# "LOCTYPT",
# "OCCTYP",
# "OCCTYPT",
# "PPTYP",
# "PPTYPT",
# "AGYTYP",
# "OCCFAM",
# "PPGROUP",
# "PAYPLAN",
# "TOATYP",
# "WSTYP",
# "AGYSUBT",
# "AGELVL",
# "LOSLVL",
# "LOC",
# "OCC",
# "PATCO",
# "SALLVL",
# "TOA",
# "WORKSCH"]
#
#for i in dropCols:
# cols.remove(i)
#
#plotCat = SampledOPMData[cols]
#display(plotCat.head())
#print("plotCat Has {0} Records".format(len(plotCat)))
#print("Number of colums = ", len(cols))
%%time
cols = list(OPMDataMerged.select_dtypes(include=['object']))
dropCols = ["LOCTYP",
"LOCTYPT",
"OCCTYP",
"OCCTYPT",
"PPTYP",
"PPTYPT",
"AGYTYP",
"OCCFAM",
"PPGROUP",
"PAYPLAN",
"TOATYP",
"WSTYP",
"AGYSUBT",
"AGELVL",
"LOSLVL",
"LOC",
"OCC",
"PATCO",
"SALLVL",
"TOA",
"WORKSCH"]
for i in dropCols:
cols.remove(i)
plotCat = OPMDataMerged[cols]
display(plotCat.head())
print("plotCat Has {0} Records".format(len(plotCat)))
print("Number of colums = ", len(cols))
| AGYSUB | SEP | DATECODE | GENDER | GSEGRD | PPGRD | AGYTYPT | AGY | AGYT | QTR | AGELVLT | LOSLVLT | LOCT | OCCFAMT | OCCT | PATCOT | PPGROUPT | PAYPLANT | SALLVLT | TOATYPT | TOAT | WSTYPT | WORKSCHT | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | AA00 | SC | 201507 | M | 11 | GS-11 | Small Independent Agencies (less than 100 empl... | AA | AA-ADMINISTRATIVE CONFERENCE OF THE UNITED STATES | 4 | 25-29 | Less than 1 year | 11-DISTRICT OF COLUMBIA | 09xx-LEGAL AND KINDRED | 0905-GENERAL ATTORNEY | Professional | Standard GSEG Pay Plans | GS-GENERAL SCHEDULE | $60,000 - $69,999 | Non-permanent | 40-Excepted Service - Schedule A | Full-time | Full-time Nonseasonal |
| 1 | AA00 | SC | 201506 | F | 15 | GS-15 | Small Independent Agencies (less than 100 empl... | AA | AA-ADMINISTRATIVE CONFERENCE OF THE UNITED STATES | 3 | 30-34 | 3 - 4 years | 11-DISTRICT OF COLUMBIA | 09xx-LEGAL AND KINDRED | 0905-GENERAL ATTORNEY | Professional | Standard GSEG Pay Plans | GS-GENERAL SCHEDULE | $120,000 - $129,999 | Permanent | 30-Excepted Service - Schedule A | Full-time | Full-time Nonseasonal |
| 2 | AF** | SA | 201503 | M | 11 | GS-11 | Cabinet Level Agencies | AF | AF-DEPARTMENT OF THE AIR FORCE | 2 | 50-54 | 3 - 4 years | 48-TEXAS | 22xx-INFORMATION TECHNOLOGY | 2210-INFORMATION TECHNOLOGY MANAGEMENT | Administrative | Standard GSEG Pay Plans | GS-GENERAL SCHEDULE | $60,000 - $69,999 | Permanent | 10-Competitive Service - Career | Full-time | Full-time Nonseasonal |
| 3 | AF02 | SD | 201506 | M | 15 | GS-15 | Cabinet Level Agencies | AF | AF-DEPARTMENT OF THE AIR FORCE | 3 | 55-59 | 35 years or more | 35-NEW MEXICO | 03xx-GENERAL ADMIN, CLERICAL, & OFFICE SVCS | 0301-MISCELLANEOUS ADMINISTRATION AND PROGRAM | Administrative | Standard GSEG Pay Plans | GS-GENERAL SCHEDULE | $150,000 - $159,999 | Permanent | 10-Competitive Service - Career | Full-time | Full-time Nonseasonal |
| 4 | AF03 | SC | 201509 | M | 13 | GS-13 | Cabinet Level Agencies | AF | AF-DEPARTMENT OF THE AIR FORCE | 4 | 50-54 | 1 - 2 years | 06-CALIFORNIA | 03xx-GENERAL ADMIN, CLERICAL, & OFFICE SVCS | 0301-MISCELLANEOUS ADMINISTRATION AND PROGRAM | Administrative | Standard GSEG Pay Plans | GS-GENERAL SCHEDULE | $90,000 - $99,999 | Permanent | 15-Competitive Service - Career-Conditional | Full-time | Full-time Nonseasonal |
plotCat Has 8170907 Records Number of colums = 23 CPU times: user 3.01 s, sys: 1.33 s, total: 4.34 s Wall time: 4.34 s
High seperation among following:
Similar separation distributions among males and females, except more terminations due to contract expiration among males
High termination due to expired appt/other among following:
Bimodal Quit distribution with outlier spike at GSEGRD 9:
Individual transfers highest among levels 11, 12, 13
Majority of distribution resides in GS values per the GSEGRD observations described above.... Are other PPGRD values of any significance? What are corporate grades all about?
Top three Agencies with separation:
High contract termination in:
While Veteran Affairs and Army both have many quits and many retirees, the Army has significantly more individual transfers (on par with retirements)
Most contract terminations in 1st and 4th quarters
Retirement peaks in 2nd quarter
Number of quits increases from one quarter to the next
*Bear in mind these are quarters from single year only so time-sensitive trends may not be applicable*
High termination due to expired appt/other among following:
Number of Quits peaks at AGELVL D
Individual transfer counts mostly trend with Quits
Retirement highest at following:
Highest Quit count for LOSLVL A (< 1 year service) which then declines for levels B and C before spiking again at level D (5-9 years service)
Same pattern is observed for contract terminations but without any significant spikes with longer service
Large individual transfer spike at LOSLVL D (5-9 years service)
Retirement starts at LOSLVL D but trends upward to J
Contract terminations comprise most California terminations among top total separation states
East Coast locations may possibly have most individual transfers, the most being in Washington DC
03xx-General Admin, clerical, and office svcs highest separation by far but indicates both high number of Quits and Retirements
Many quits in 06xx-Medical
04xx-Natural Resources again indicates high number of contract terminations
01xx-Social Science has even number of Quits and retirements
Results skewed by GS
Should model full time only
def subCountPlot(att1, att2, thresh):
counts = plotCat.groupby([att1, att2]).size().unstack(fill_value=0) # Get att1 sizes by att2
counts = pd.concat([counts,counts.sum(axis=1)], axis=1) # Calculate total for each att1 value and append total as new column
counts.rename(columns={0:"Total"}, inplace=True)
top = counts[counts["Total"] > thresh].index.tolist() # Obtain att1 values where total surpasses threshold
zoom = plotCat[plotCat[att1].isin(top)] # Subset data to only the top att1 values
f, (ax1, ax2) = plt.subplots(ncols=2, figsize=(20, 10), sharey=False)
sns.countplot(y=att1, data=zoom, color="blue", ax=ax1); # Dark blue signifies zoomed data
sns.countplot(y=att1, data=zoom, hue=att2, palette="hls", ax=ax2);
def percBarPlot(att1, att2, numColors):
# Create count by att1 and att2
counts = plotCat.groupby([att1, att2]).size().unstack(fill_value=0) # Get att1 sizes by att2
counts = pd.concat([counts,counts.sum(axis=1)], axis=1) # Calculate total for each att1 value and append total as new column
counts.rename(columns={0:"Total"}, inplace=True)
#counts.drop('Total', axis=1).plot(kind='bar', stacked=True)
# create cmap from sns color palette
my_cmap = ListedColormap(sns.color_palette('hls', numColors).as_hex())
# Create and plot percentage by att1 and att2
nest1 = []
for i in counts.values:
nest2 = []
for j in i:
nest2.append(float(j/(i[len(i)-1:]))*100)
nest1.append(nest2)
perc = pd.DataFrame(nest1)
perc = perc.set_index(counts.index.values)
perc.columns = counts.columns
perc.drop('Total', axis=1).plot(kind='bar', stacked=True, ylim=(0,100), figsize={13,6}, title=att1+' Percentage Plot', colormap=my_cmap)
temp = cols[:4] # for quick visualization debug only; may delete once complete
%%time
for i in cols:
if i != 'SEP':
plt.figure(i) # Required to create new figure each loop rather than drawing over previous object
f, (ax1, ax2) = plt.subplots(ncols=2, figsize=(20, 10), sharey=False)
sns.countplot(y=i, data=plotCat, color="lightblue", ax=ax1);
sns.countplot(y=i, data=plotCat, hue="SEP", palette="hls", ax=ax2);
if i == 'AGYSUB':
subCountPlot(i, 'SEP', 10000)
elif i == 'LOCT':
subCountPlot(i, 'SEP', 4000)
elif i == 'OCCT':
subCountPlot(i, 'SEP', 2000)
elif i == 'PPGRD':
subCountPlot(i, 'SEP', 6000)
elif i == 'AGYT':
subCountPlot(i, 'SEP', 3000)
/usr/local/es7/lib/python3.5/site-packages/matplotlib/pyplot.py:524: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`). max_open_warning, RuntimeWarning)
CPU times: user 10min 8s, sys: 13 s, total: 10min 21s Wall time: 10min 20s
<matplotlib.figure.Figure at 0x7f72859fb780>
/usr/local/es7/lib/python3.5/site-packages/matplotlib/font_manager.py:1297: UserWarning: findfont: Font family ['sans-serif'] not found. Falling back to DejaVu Sans (prop.get_family(), self.defaultFamily[fontext]))
<matplotlib.figure.Figure at 0x7f71ce323d68>
<matplotlib.figure.Figure at 0x7f71ccb748d0>
<matplotlib.figure.Figure at 0x7f71ce7facf8>
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<matplotlib.figure.Figure at 0x7f71aaa91cc0>
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<matplotlib.figure.Figure at 0x7f7198f39f28>
<matplotlib.figure.Figure at 0x7f7198ac1438>
%%time
for i in cols:
if i != 'SEP':
percBarPlot(i, 'SEP', len(plotCat.SEP.drop_duplicates()))
/usr/local/es7/lib/python3.5/site-packages/matplotlib/pyplot.py:524: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`). max_open_warning, RuntimeWarning)
CPU times: user 1min 12s, sys: 3.72 s, total: 1min 15s Wall time: 1min 15s
/usr/local/es7/lib/python3.5/site-packages/matplotlib/font_manager.py:1297: UserWarning: findfont: Font family ['sans-serif'] not found. Falling back to DejaVu Sans (prop.get_family(), self.defaultFamily[fontext]))
percBarPlot('GSEGRD', 'SALLVLT', len(plotCat.SALLVLT.drop_duplicates()))
/usr/local/es7/lib/python3.5/site-packages/matplotlib/font_manager.py:1297: UserWarning: findfont: Font family ['sans-serif'] not found. Falling back to DejaVu Sans (prop.get_family(), self.defaultFamily[fontext]))
percBarPlot('PATCOT', 'SALLVLT', len(plotCat.SALLVLT.drop_duplicates()))
/usr/local/es7/lib/python3.5/site-packages/matplotlib/font_manager.py:1297: UserWarning: findfont: Font family ['sans-serif'] not found. Falling back to DejaVu Sans (prop.get_family(), self.defaultFamily[fontext]))
%%time
sns.set(style="whitegrid", palette="pastel", color_codes=True)
sns.violinplot(x="PATCOT", y="SALARY", hue="GENDER", data=OPMDataMerged[OPMDataMerged.GENDER != 'Z'], split=True,
inner="quart", palette={"M": "b", "F": "pink"})
sns.despine(left=True)
CPU times: user 22.5 s, sys: 1min 4s, total: 1min 26s Wall time: 14 s
/usr/local/es7/lib/python3.5/site-packages/matplotlib/font_manager.py:1297: UserWarning: findfont: Font family ['sans-serif'] not found. Falling back to DejaVu Sans (prop.get_family(), self.defaultFamily[fontext]))
%%time
# Draw a nested violinplot and split the violins for easier comparison
sns.violinplot(x="SEP", y="SALARY", hue="GENDER", data=OPMDataMerged[OPMDataMerged.GENDER != 'Z'], split=True,
inner="quart", palette={"M": "b", "F": "pink"})
sns.despine(left=True)
CPU times: user 17.1 s, sys: 38.5 s, total: 55.6 s Wall time: 12.4 s
/usr/local/es7/lib/python3.5/site-packages/matplotlib/font_manager.py:1297: UserWarning: findfont: Font family ['sans-serif'] not found. Falling back to DejaVu Sans (prop.get_family(), self.defaultFamily[fontext]))
%%time
sns.factorplot(x="SEP", y="SALARY", hue="GENDER", col="PATCOT",
data=OPMDataMerged[OPMDataMerged.GENDER != 'Z'],
kind="violin", split=True, aspect=.4, size=10);
/usr/local/es7/lib/python3.5/site-packages/matplotlib/font_manager.py:1297: UserWarning: findfont: Font family ['sans-serif'] not found. Falling back to DejaVu Sans (prop.get_family(), self.defaultFamily[fontext]))
CPU times: user 35.3 s, sys: 16.7 s, total: 52 s Wall time: 38.1 s
<seaborn.axisgrid.FacetGrid at 0x7f71a1bebfd0>
#%%time
#
#sns.factorplot(x="SEP", y="SALARY", col="PATCOT", data=OPMDataMerged,
# kind="violin", split=True, aspect=.4, size=10, palette = "hls");
#%%time
#
#g = sns.PairGrid(data=OPMDataMerged,
# x_vars=["SEP","PATCOT"],
# y_vars=["SALARY", "LOS", "LowerLimitAge", "YearsToRetirement"],
# aspect=1, size=10)
#g.map(sns.violinplot, palette="pastel");
del(plotNumeric, plotCat)
After analyzing the above plots for our categorical data, we have decided to narrow our focus due to the large variability in the dataset. We take the below actions on our dataset:
In addition, we have opted to remove the below attributes for model generation:
Our goal is to limit our focus to Professional occupations, build a model, then test that generated model on the Administration segment of the population.
%%time
print(len(OPMDataMerged))
#Removing Attributes
cols = list(OPMDataMerged.columns)
dropCols = ["QTR",
"AGYTYP",
"AGYTYPT",
"AGY",
"AGYT",
"AGYSUB",
"AGYSUBT",
"GENDER",
"COUNT",
"PAYPLAN",
"PAYPLANT",
"PPGRD",
"LOSLVL",
"LOSLVLT",
"SALLVL",
"SALLVLT",
"OCC",
"OCCT"]
for i in dropCols:
if i in cols:
cols.remove(i)
OPMDataMerged = OPMDataMerged[cols]
# Keep only Full-time Nonseasonal observations
OPMDataMerged = OPMDataMerged[OPMDataMerged["WORKSCH"] == "F"]
#Remove the location US-SUPPRESSED (SEE DATA DEFINITIONS)
OPMDataMerged = OPMDataMerged[OPMDataMerged["LOC"] != "US"]
#Keep only General Schedele Grades above 7.
OPMDataMerged["GSEGRD"] = OPMDataMerged["GSEGRD"].apply(pd.to_numeric)
OPMDataMerged = OPMDataMerged[OPMDataMerged["GSEGRD"] >= 7]
#Focus model generation on White Collar Jobs only
OPMDataMerged = OPMDataMerged[OPMDataMerged["OCCTYP"] == "1"]
#Create a Training set for the Professional PATCO value, and a Testing set for Administration
OPMDataMergedProf = OPMDataMerged[OPMDataMerged["PATCO"] == "1"]
OPMDataMergedAdmin = OPMDataMerged[OPMDataMerged["PATCO"] == "2"]
8170907 CPU times: user 2min 1s, sys: 3.49 s, total: 2min 5s Wall time: 2min 5s
display(OPMDataMergedProf.head())
print(len(OPMDataMergedProf))
| SEP | DATECODE | AGELVL | GSEGRD | LOC | PATCO | TOA | WORKSCH | SALARY | LOS | AGELVLT | LOCTYP | LOCTYPT | LOCT | OCCTYP | OCCTYPT | OCCFAM | OCCFAMT | PATCOT | PPTYP | PPTYPT | PPGROUP | PPGROUPT | TOATYP | TOATYPT | TOAT | WSTYP | WSTYPT | WORKSCHT | SEPCount_EFDATE_OCC | SEPCount_EFDATE_LOC | IndAvgSalary | SalaryOverUnderIndAvg | LowerLimitAge | YearsToRetirement | BLS_FEDERAL_OtherSep_Rate | BLS_FEDERAL_Quits_Rate | BLS_FEDERAL_TotalSep_Level | BLS_FEDERAL_JobOpenings_Rate | BLS_FEDERAL_OtherSep_Level | BLS_FEDERAL_Quits_Level | BLS_FEDERAL_JobOpenings_Level | BLS_FEDERAL_Layoffs_Rate | BLS_FEDERAL_Layoffs_Level | BLS_FEDERAL_TotalSep_Rate | SALARYLog | LOSSqrt | SEPCount_EFDATE_OCCLog | SEPCount_EFDATE_LOCLog | IndAvgSalaryLog | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | SC | 201507 | C | 11.0 | 11 | 1 | 40 | F | 63722.0 | 0.8 | 25-29 | 1 | United States | 11-DISTRICT OF COLUMBIA | 1 | White Collar | 09 | 09xx-LEGAL AND KINDRED | Professional | 1 | General Schedule and Equivalently Graded (GSEG... | 11 | Standard GSEG Pay Plans | 2 | Non-permanent | 40-Excepted Service - Schedule A | 1 | Full-time | Full-time Nonseasonal | 205.0 | 1319 | 64540.593830 | -818.593830 | 25.0 | 32.0 | 0.4 | 0.5 | 34 | 2.6 | 11 | 13 | 74 | 0.4 | 10 | 1.2 | 11.062285 | 0.894427 | 5.323010 | 7.184629 | 11.075050 |
| 1 | SC | 201506 | D | 15.0 | 11 | 1 | 30 | F | 126245.0 | 4.8 | 30-34 | 1 | United States | 11-DISTRICT OF COLUMBIA | 1 | White Collar | 09 | 09xx-LEGAL AND KINDRED | Professional | 1 | General Schedule and Equivalently Graded (GSEG... | 11 | Standard GSEG Pay Plans | 1 | Permanent | 30-Excepted Service - Schedule A | 1 | Full-time | Full-time Nonseasonal | 207.0 | 1132 | 149864.298504 | -23619.298504 | 30.0 | 27.0 | 0.4 | 0.5 | 34 | 2.3 | 12 | 13 | 65 | 0.4 | 10 | 1.2 | 11.745980 | 2.190890 | 5.332719 | 7.031741 | 11.917485 |
| 8 | SD | 201509 | I | 14.0 | 06 | 1 | 10 | F | 135500.0 | 14.3 | 55-59 | 1 | United States | 06-CALIFORNIA | 1 | White Collar | 08 | 08xx-ENGINEERING AND ARCHITECTURE | Professional | 1 | General Schedule and Equivalently Graded (GSEG... | 11 | Standard GSEG Pay Plans | 1 | Permanent | 10-Competitive Service - Career | 1 | Full-time | Full-time Nonseasonal | 122.0 | 1853 | 125803.916312 | 9696.083688 | 55.0 | 2.0 | 0.4 | 0.5 | 38 | 1.9 | 12 | 14 | 55 | 0.4 | 12 | 1.4 | 11.816727 | 3.781534 | 4.804021 | 7.524561 | 11.742480 |
| 11 | SD | 201503 | J | 14.0 | 08 | 1 | 10 | F | 128223.0 | 20.6 | 60-64 | 1 | United States | 08-COLORADO | 1 | White Collar | 08 | 08xx-ENGINEERING AND ARCHITECTURE | Professional | 1 | General Schedule and Equivalently Graded (GSEG... | 11 | Standard GSEG Pay Plans | 1 | Permanent | 10-Competitive Service - Career | 1 | Full-time | Full-time Nonseasonal | 92.0 | 329 | 126328.546349 | 1894.453651 | 60.0 | -3.0 | 0.3 | 0.4 | 31 | 3.0 | 9 | 10 | 86 | 0.5 | 12 | 1.1 | 11.761526 | 4.538722 | 4.521789 | 5.796058 | 11.746641 |
| 14 | SA | 201508 | H | 13.0 | 06 | 1 | 10 | F | 111566.0 | 24.3 | 50-54 | 1 | United States | 06-CALIFORNIA | 1 | White Collar | 08 | 08xx-ENGINEERING AND ARCHITECTURE | Professional | 1 | General Schedule and Equivalently Graded (GSEG... | 11 | Standard GSEG Pay Plans | 1 | Permanent | 10-Competitive Service - Career | 1 | Full-time | Full-time Nonseasonal | 110.0 | 1606 | 105047.296509 | 6518.703491 | 50.0 | 7.0 | 0.5 | 0.6 | 41 | 2.3 | 14 | 17 | 67 | 0.3 | 10 | 1.5 | 11.622372 | 4.929503 | 4.700480 | 7.381502 | 11.562166 |
1283383
display(OPMDataMergedAdmin.head())
print(len(OPMDataMergedAdmin))
| SEP | DATECODE | AGELVL | GSEGRD | LOC | PATCO | TOA | WORKSCH | SALARY | LOS | AGELVLT | LOCTYP | LOCTYPT | LOCT | OCCTYP | OCCTYPT | OCCFAM | OCCFAMT | PATCOT | PPTYP | PPTYPT | PPGROUP | PPGROUPT | TOATYP | TOATYPT | TOAT | WSTYP | WSTYPT | WORKSCHT | SEPCount_EFDATE_OCC | SEPCount_EFDATE_LOC | IndAvgSalary | SalaryOverUnderIndAvg | LowerLimitAge | YearsToRetirement | BLS_FEDERAL_OtherSep_Rate | BLS_FEDERAL_Quits_Rate | BLS_FEDERAL_TotalSep_Level | BLS_FEDERAL_JobOpenings_Rate | BLS_FEDERAL_OtherSep_Level | BLS_FEDERAL_Quits_Level | BLS_FEDERAL_JobOpenings_Level | BLS_FEDERAL_Layoffs_Rate | BLS_FEDERAL_Layoffs_Level | BLS_FEDERAL_TotalSep_Rate | SALARYLog | LOSSqrt | SEPCount_EFDATE_OCCLog | SEPCount_EFDATE_LOCLog | IndAvgSalaryLog | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 2 | SA | 201503 | H | 11.0 | 48 | 2 | 10 | F | 66585.0 | 4.9 | 50-54 | 1 | United States | 48-TEXAS | 1 | White Collar | 22 | 22xx-INFORMATION TECHNOLOGY | Administrative | 1 | General Schedule and Equivalently Graded (GSEG... | 11 | Standard GSEG Pay Plans | 1 | Permanent | 10-Competitive Service - Career | 1 | Full-time | Full-time Nonseasonal | 439.0 | 1087 | 71530.963755 | -4945.963755 | 50.0 | 7.0 | 0.3 | 0.4 | 31 | 3.0 | 9 | 10 | 86 | 0.5 | 12 | 1.1 | 11.106235 | 2.213594 | 6.084499 | 6.991177 | 11.177886 |
| 3 | SD | 201506 | I | 15.0 | 35 | 2 | 10 | F | 156737.0 | 39.8 | 55-59 | 1 | United States | 35-NEW MEXICO | 1 | White Collar | 03 | 03xx-GENERAL ADMIN, CLERICAL, & OFFICE SVCS | Administrative | 1 | General Schedule and Equivalently Graded (GSEG... | 11 | Standard GSEG Pay Plans | 1 | Permanent | 10-Competitive Service - Career | 1 | Full-time | Full-time Nonseasonal | 670.0 | 265 | 146735.220304 | 10001.779696 | 55.0 | 2.0 | 0.4 | 0.5 | 34 | 2.3 | 12 | 13 | 65 | 0.4 | 10 | 1.2 | 11.962325 | 6.308724 | 6.507278 | 5.579730 | 11.896385 |
| 4 | SC | 201509 | H | 13.0 | 06 | 2 | 15 | F | 92973.0 | 1.0 | 50-54 | 1 | United States | 06-CALIFORNIA | 1 | White Collar | 03 | 03xx-GENERAL ADMIN, CLERICAL, & OFFICE SVCS | Administrative | 1 | General Schedule and Equivalently Graded (GSEG... | 11 | Standard GSEG Pay Plans | 1 | Permanent | 15-Competitive Service - Career-Conditional | 1 | Full-time | Full-time Nonseasonal | 721.0 | 1853 | 101641.124025 | -8668.124025 | 50.0 | 7.0 | 0.4 | 0.5 | 38 | 1.9 | 12 | 14 | 55 | 0.4 | 12 | 1.4 | 11.440064 | 1.000000 | 6.580639 | 7.524561 | 11.529203 |
| 5 | SD | 201509 | I | 13.0 | 35 | 2 | 10 | F | 102943.0 | 11.3 | 55-59 | 1 | United States | 35-NEW MEXICO | 1 | White Collar | 03 | 03xx-GENERAL ADMIN, CLERICAL, & OFFICE SVCS | Administrative | 1 | General Schedule and Equivalently Graded (GSEG... | 11 | Standard GSEG Pay Plans | 1 | Permanent | 10-Competitive Service - Career | 1 | Full-time | Full-time Nonseasonal | 721.0 | 557 | 101641.124025 | 1301.875975 | 55.0 | 2.0 | 0.4 | 0.5 | 38 | 1.9 | 12 | 14 | 55 | 0.4 | 12 | 1.4 | 11.541931 | 3.361547 | 6.580639 | 6.322565 | 11.529203 |
| 10 | SA | 201502 | F | 11.0 | 35 | 2 | 15 | F | 70621.0 | 9.7 | 40-44 | 1 | United States | 35-NEW MEXICO | 1 | White Collar | 22 | 22xx-INFORMATION TECHNOLOGY | Administrative | 1 | General Schedule and Equivalently Graded (GSEG... | 11 | Standard GSEG Pay Plans | 1 | Permanent | 15-Competitive Service - Career-Conditional | 1 | Full-time | Full-time Nonseasonal | 390.0 | 169 | 71530.963755 | -909.963755 | 40.0 | 17.0 | 0.3 | 0.4 | 26 | 3.2 | 8 | 10 | 91 | 0.3 | 8 | 1.0 | 11.165083 | 3.114482 | 5.966147 | 5.129899 | 11.177886 |
2131840
#curious on stratum SEP counts for full remaining data
stratum = pd.DataFrame({'StratCount' : OPMDataMerged.groupby(["SEP"]).size()}).reset_index()
display(stratum)
| SEP | StratCount | |
|---|---|---|
| 0 | NS | 4101470 |
| 1 | SA | 17983 |
| 2 | SB | 257 |
| 3 | SC | 22021 |
| 4 | SD | 35304 |
| 5 | SE | 876 |
| 6 | SF | 1988 |
| 7 | SG | 788 |
| 8 | SH | 79 |
| 9 | SI | 2727 |
| 10 | SJ | 5926 |
| 11 | SK | 1575 |
| 12 | SL | 18 |
#Assess Stratum SEP Counts for Prof, for use in sampling
maxSize=7500
stratumProf = pd.DataFrame({'StratCount' : OPMDataMergedProf.groupby(["SEP"]).size()}).reset_index()
stratumProf.loc[stratumProf["StratCount"]>maxSize,"StratCountSample"] = maxSize
stratumProf.loc[stratumProf["StratCount"]<=maxSize,"StratCountSample"] = stratumProf["StratCount"]
#else: stratum["StratCountSample"] = stratum["StratCount"]
display(stratumProf)
| SEP | StratCount | StratCountSample | |
|---|---|---|---|
| 0 | NS | 1259283 | 7500.0 |
| 1 | SA | 5463 | 5463.0 |
| 2 | SB | 85 | 85.0 |
| 3 | SC | 7423 | 7423.0 |
| 4 | SD | 8881 | 7500.0 |
| 5 | SE | 167 | 167.0 |
| 6 | SF | 338 | 338.0 |
| 7 | SG | 90 | 90.0 |
| 8 | SH | 15 | 15.0 |
| 9 | SI | 631 | 631.0 |
| 10 | SJ | 645 | 645.0 |
| 11 | SK | 355 | 355.0 |
| 12 | SL | 7 | 7.0 |
#Assess Stratum SEP Counts for Admin, for use in sampling
maxSize=7500
stratumAdmin = pd.DataFrame({'StratCount' : OPMDataMergedAdmin.groupby(["SEP"]).size()}).reset_index()
stratumAdmin.loc[stratumAdmin["StratCount"]>maxSize,"StratCountSample"] = maxSize
stratumAdmin.loc[stratumAdmin["StratCount"]<=maxSize,"StratCountSample"] = stratumAdmin["StratCount"]
#else: stratum["StratCountSample"] = stratum["StratCount"]
display(stratumAdmin)
| SEP | StratCount | StratCountSample | |
|---|---|---|---|
| 0 | NS | 2087084 | 7500.0 |
| 1 | SA | 9252 | 7500.0 |
| 2 | SB | 145 | 145.0 |
| 3 | SC | 9156 | 7500.0 |
| 4 | SD | 19402 | 7500.0 |
| 5 | SE | 555 | 555.0 |
| 6 | SF | 995 | 995.0 |
| 7 | SG | 414 | 414.0 |
| 8 | SH | 39 | 39.0 |
| 9 | SI | 1196 | 1196.0 |
| 10 | SJ | 2771 | 2771.0 |
| 11 | SK | 820 | 820.0 |
| 12 | SL | 11 | 11.0 |
%%time
def aggStratPop(stratum, OPMDataMerged):
AggStrat = []
for i in range(0,len(stratum)):
sep = stratum["SEP"].ix[i]
StratCountSample = stratum["StratCountSample"].ix[i]
print("Stratum Sample Size Calculations for SEP: {}".format(sep))
AggStrat.append(pd.DataFrame({'StratCount' : OPMDataMerged[OPMDataMerged["SEP"]==sep].groupby(["DATECODE", "AGELVL"]).size()}).reset_index())
AggStrat[i]["SEP"] = sep
AggStrat[i]["TotalCount"] = len(OPMDataMerged[OPMDataMerged["SEP"]==sep])
AggStrat[i]["p"] = AggStrat[i]["StratCount"] / AggStrat[i]["TotalCount"]
AggStrat[i]["StratCountSample"] = StratCountSample
AggStrat[i]["StratSampleSize"] = round(AggStrat[i]["p"] * StratCountSample).apply(int)
display(AggStrat[i].head())
print("totalStratumSampleSize: ", AggStrat[i]["StratSampleSize"].sum())
#print(len(AggStrat[i]))
return AggStrat
CPU times: user 5 µs, sys: 1e+03 ns, total: 6 µs Wall time: 10 µs
def SampleStrata(stratum, OPMDataMerged, FileName):
AggStrat = aggStratPop(stratum, OPMDataMerged)
SampledOPMStratumDataList = []
for i,StratSampleSize in enumerate(AggStrat):
SampledOPMStratumData = []
for j in range(0,len(StratSampleSize)):
SEP = StratSampleSize["SEP"].ix[j]
DATECODE = StratSampleSize["DATECODE"].ix[j]
AGELVL = StratSampleSize["AGELVL"].ix[j]
SampleSize = StratSampleSize["StratSampleSize"].ix[j]
print(SEP, DATECODE, AGELVL, SampleSize)
SampledOPMStratumDataList.append(OPMDataMerged[(OPMDataMerged["SEP"]==SEP)
& (OPMDataMerged["DATECODE"]==DATECODE)
& (OPMDataMerged["AGELVL"]==AGELVL)].sample(SampleSize, random_state=SampleSize))
SampledOPMStratumData.append(pd.concat(SampledOPMStratumDataList))
clear_display()
SampledOPMData = pd.concat(SampledOPMStratumData).reset_index()
del SampledOPMData["index"]
pickleObject(SampledOPMData, FileName)
clear_display()
return SampledOPMData
Using a seed value equal to each strata sample size, we take random samples according to the computed sizes above. We loop through each Separation Type's Aggregated Strata Sample Sizes; Identify all observations matching on Datecode, Separation Type, and AgeLevel; and finally sample those observations with the computed sample size.
%%time
##Prof Data Sampling
if os.path.isfile(PickleJarPath+"/SampledOPMDataProf.pkl"):
print("Found the File! Loading Pickle Now!")
SampledOPMDataProf = unpickleObject("SampledOPMDataProf")
else:
SampledOPMDataProf= SampleStrata(stratumProf, OPMDataMergedProf, "SampledOPMDataProf")
Found the File! Loading Pickle Now! CPU times: user 26.3 ms, sys: 3.21 ms, total: 29.5 ms Wall time: 29.3 ms
%%time
print(len(SampledOPMDataProf))
display(SampledOPMDataProf.head())
display(pd.DataFrame({'StratCount' : SampledOPMDataProf.groupby(["SEP"]).size()}).reset_index())
30227
| SEP | DATECODE | AGELVL | GSEGRD | LOC | PATCO | TOA | WORKSCH | SALARY | LOS | AGELVLT | LOCTYP | LOCTYPT | LOCT | OCCTYP | OCCTYPT | OCCFAM | OCCFAMT | PATCOT | PPTYP | PPTYPT | PPGROUP | PPGROUPT | TOATYP | TOATYPT | TOAT | WSTYP | WSTYPT | WORKSCHT | SEPCount_EFDATE_OCC | SEPCount_EFDATE_LOC | IndAvgSalary | SalaryOverUnderIndAvg | LowerLimitAge | YearsToRetirement | BLS_FEDERAL_OtherSep_Rate | BLS_FEDERAL_Quits_Rate | BLS_FEDERAL_TotalSep_Level | BLS_FEDERAL_JobOpenings_Rate | BLS_FEDERAL_OtherSep_Level | BLS_FEDERAL_Quits_Level | BLS_FEDERAL_JobOpenings_Level | BLS_FEDERAL_Layoffs_Rate | BLS_FEDERAL_Layoffs_Level | BLS_FEDERAL_TotalSep_Rate | SALARYLog | LOSSqrt | SEPCount_EFDATE_OCCLog | SEPCount_EFDATE_LOCLog | IndAvgSalaryLog | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | NS | 201412 | B | 11.0 | 51 | 1 | 15 | F | 77658.0 | 1.5 | 20-24 | 1 | United States | 51-VIRGINIA | 1 | White Collar | 12 | 12xx-COPYRIGHT, PATENT, AND TRADE-MARK | Professional | 1 | General Schedule and Equivalently Graded (GSEG... | 11 | Standard GSEG Pay Plans | 1 | Permanent | 15-Competitive Service - Career-Conditional | 1 | Full-time | Full-time Nonseasonal | 26.0 | 1133 | 78919.462629 | -1261.462629 | 20.0 | 37.0 | 0.5 | 0.4 | 30 | 2.2 | 12 | 10 | 62 | 0.3 | 7 | 1.1 | 11.260070 | 1.224745 | 3.258097 | 7.032624 | 11.276183 |
| 1 | NS | 201412 | B | 9.0 | 11 | 1 | 15 | F | 52146.0 | 2.9 | 20-24 | 1 | United States | 11-DISTRICT OF COLUMBIA | 1 | White Collar | 11 | 11xx-BUSINESS AND INDUSTRY | Professional | 1 | General Schedule and Equivalently Graded (GSEG... | 11 | Standard GSEG Pay Plans | 1 | Permanent | 15-Competitive Service - Career-Conditional | 1 | Full-time | Full-time Nonseasonal | 274.0 | 1260 | 52460.545881 | -314.545881 | 20.0 | 37.0 | 0.5 | 0.4 | 30 | 2.2 | 12 | 10 | 62 | 0.3 | 7 | 1.1 | 10.861803 | 1.702939 | 5.613128 | 7.138867 | 10.867817 |
| 2 | NS | 201412 | B | 9.0 | 53 | 1 | 15 | F | 57368.0 | 0.5 | 20-24 | 1 | United States | 53-WASHINGTON | 1 | White Collar | 08 | 08xx-ENGINEERING AND ARCHITECTURE | Professional | 1 | General Schedule and Equivalently Graded (GSEG... | 11 | Standard GSEG Pay Plans | 1 | Permanent | 15-Competitive Service - Career-Conditional | 1 | Full-time | Full-time Nonseasonal | 7.0 | 555 | 58563.540541 | -1195.540541 | 20.0 | 37.0 | 0.5 | 0.4 | 30 | 2.2 | 12 | 10 | 62 | 0.3 | 7 | 1.1 | 10.957242 | 0.707107 | 1.945910 | 6.318968 | 10.977868 |
| 3 | NS | 201412 | B | 7.0 | 25 | 1 | 15 | F | 42830.0 | 1.5 | 20-24 | 1 | United States | 25-MASSACHUSETTS | 1 | White Collar | 05 | 05xx-ACCOUNTING AND BUDGET | Professional | 1 | General Schedule and Equivalently Graded (GSEG... | 11 | Standard GSEG Pay Plans | 1 | Permanent | 15-Competitive Service - Career-Conditional | 1 | Full-time | Full-time Nonseasonal | 82.0 | 304 | 43211.010135 | -381.010135 | 20.0 | 37.0 | 0.5 | 0.4 | 30 | 2.2 | 12 | 10 | 62 | 0.3 | 7 | 1.1 | 10.664994 | 1.224745 | 4.406719 | 5.717028 | 10.673851 |
| 4 | NS | 201412 | B | 7.0 | 53 | 1 | 15 | F | 44615.0 | 2.5 | 20-24 | 1 | United States | 53-WASHINGTON | 1 | White Collar | 08 | 08xx-ENGINEERING AND ARCHITECTURE | Professional | 1 | General Schedule and Equivalently Graded (GSEG... | 11 | Standard GSEG Pay Plans | 1 | Permanent | 15-Competitive Service - Career-Conditional | 1 | Full-time | Full-time Nonseasonal | 50.0 | 555 | 49854.574586 | -5239.574586 | 20.0 | 37.0 | 0.5 | 0.4 | 30 | 2.2 | 12 | 10 | 62 | 0.3 | 7 | 1.1 | 10.705825 | 1.581139 | 3.912023 | 6.318968 | 10.816866 |
| SEP | StratCount | |
|---|---|---|
| 0 | NS | 7501 |
| 1 | SA | 5463 |
| 2 | SB | 85 |
| 3 | SC | 7423 |
| 4 | SD | 7507 |
| 5 | SE | 167 |
| 6 | SF | 338 |
| 7 | SG | 90 |
| 8 | SH | 15 |
| 9 | SI | 631 |
| 10 | SJ | 645 |
| 11 | SK | 355 |
| 12 | SL | 7 |
CPU times: user 52.8 ms, sys: 538 µs, total: 53.4 ms Wall time: 48 ms
%%time
#### Analyze Missing Values
filtered_msnoData = msno.nullity_sort(msno.nullity_filter(SampledOPMDataProf, filter='bottom', n=15, p=0.999), sort='descending')
msno.matrix(filtered_msnoData)
del filtered_msnoData
/usr/local/es7/lib/python3.5/site-packages/matplotlib/axes/_base.py:2903: UserWarning: Attempting to set identical left==right results in singular transformations; automatically expanding. left=-0.5, right=-0.5 'left=%s, right=%s') % (left, right)) /usr/local/es7/lib/python3.5/site-packages/matplotlib/font_manager.py:1297: UserWarning: findfont: Font family ['sans-serif'] not found. Falling back to DejaVu Sans (prop.get_family(), self.defaultFamily[fontext]))
CPU times: user 333 ms, sys: 248 ms, total: 581 ms Wall time: 296 ms
%%time
##Admin Data Sampling
if os.path.isfile(PickleJarPath+"/SampledOPMDataAdmin.pkl"):
print("Found the File! Loading Pickle Now!")
SampledOPMDataAdmin = unpickleObject("SampledOPMDataAdmin")
else:
SampledOPMDataAdmin= SampleStrata(stratumAdmin, OPMDataMergedAdmin, "SampledOPMDataAdmin")
Found the File! Loading Pickle Now! CPU times: user 43 ms, sys: 62 ms, total: 105 ms Wall time: 34.7 ms
%%time
print(len(SampledOPMDataAdmin))
display(SampledOPMDataAdmin.head())
display(pd.DataFrame({'StratCount' : SampledOPMDataAdmin.groupby(["SEP"]).size()}).reset_index())
36956
| SEP | DATECODE | AGELVL | GSEGRD | LOC | PATCO | TOA | WORKSCH | SALARY | LOS | AGELVLT | LOCTYP | LOCTYPT | LOCT | OCCTYP | OCCTYPT | OCCFAM | OCCFAMT | PATCOT | PPTYP | PPTYPT | PPGROUP | PPGROUPT | TOATYP | TOATYPT | TOAT | WSTYP | WSTYPT | WORKSCHT | SEPCount_EFDATE_OCC | SEPCount_EFDATE_LOC | IndAvgSalary | SalaryOverUnderIndAvg | LowerLimitAge | YearsToRetirement | BLS_FEDERAL_OtherSep_Rate | BLS_FEDERAL_Quits_Rate | BLS_FEDERAL_TotalSep_Level | BLS_FEDERAL_JobOpenings_Rate | BLS_FEDERAL_OtherSep_Level | BLS_FEDERAL_Quits_Level | BLS_FEDERAL_JobOpenings_Level | BLS_FEDERAL_Layoffs_Rate | BLS_FEDERAL_Layoffs_Level | BLS_FEDERAL_TotalSep_Rate | SALARYLog | LOSSqrt | SEPCount_EFDATE_OCCLog | SEPCount_EFDATE_LOCLog | IndAvgSalaryLog | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | NS | 201412 | B | 7.0 | 11 | 2 | 38 | F | 42631.0 | 0.1 | 20-24 | 1 | United States | 11-DISTRICT OF COLUMBIA | 1 | White Collar | 03 | 03xx-GENERAL ADMIN, CLERICAL, & OFFICE SVCS | Administrative | 1 | General Schedule and Equivalently Graded (GSEG... | 11 | Standard GSEG Pay Plans | 1 | Permanent | 38-Excepted Service - Other | 1 | Full-time | Full-time Nonseasonal | 774.0 | 1260 | 44891.572603 | -2260.572603 | 20.0 | 37.0 | 0.5 | 0.4 | 30 | 2.2 | 12 | 10 | 62 | 0.3 | 7 | 1.1 | 10.660337 | 0.316228 | 6.651572 | 7.138867 | 10.712005 |
| 1 | NS | 201412 | B | 7.0 | 51 | 2 | 15 | F | 42631.0 | 5.0 | 20-24 | 1 | United States | 51-VIRGINIA | 1 | White Collar | 11 | 11xx-BUSINESS AND INDUSTRY | Administrative | 1 | General Schedule and Equivalently Graded (GSEG... | 11 | Standard GSEG Pay Plans | 1 | Permanent | 15-Competitive Service - Career-Conditional | 1 | Full-time | Full-time Nonseasonal | 14.0 | 1133 | 45621.916667 | -2990.916667 | 20.0 | 37.0 | 0.5 | 0.4 | 30 | 2.2 | 12 | 10 | 62 | 0.3 | 7 | 1.1 | 10.660337 | 2.236068 | 2.639057 | 7.032624 | 10.728144 |
| 2 | NS | 201412 | B | 9.0 | 51 | 2 | 10 | F | 48893.0 | 3.4 | 20-24 | 1 | United States | 51-VIRGINIA | 1 | White Collar | 03 | 03xx-GENERAL ADMIN, CLERICAL, & OFFICE SVCS | Administrative | 1 | General Schedule and Equivalently Graded (GSEG... | 11 | Standard GSEG Pay Plans | 1 | Permanent | 10-Competitive Service - Career | 1 | Full-time | Full-time Nonseasonal | 774.0 | 1133 | 57175.047502 | -8282.047502 | 20.0 | 37.0 | 0.5 | 0.4 | 30 | 2.2 | 12 | 10 | 62 | 0.3 | 7 | 1.1 | 10.797390 | 1.843909 | 6.651572 | 7.032624 | 10.953873 |
| 3 | NS | 201412 | B | 9.0 | 26 | 2 | 38 | F | 53828.0 | 1.7 | 20-24 | 1 | United States | 26-MICHIGAN | 1 | White Collar | 21 | 21xx-TRANSPORTATION | Administrative | 1 | General Schedule and Equivalently Graded (GSEG... | 11 | Standard GSEG Pay Plans | 1 | Permanent | 38-Excepted Service - Other | 1 | Full-time | Full-time Nonseasonal | 80.0 | 214 | 56335.117117 | -2507.117117 | 20.0 | 37.0 | 0.5 | 0.4 | 30 | 2.2 | 12 | 10 | 62 | 0.3 | 7 | 1.1 | 10.893549 | 1.303840 | 4.382027 | 5.365976 | 10.939073 |
| 4 | NS | 201412 | B | 7.0 | 34 | 2 | 48 | F | 41797.0 | 0.8 | 20-24 | 1 | United States | 34-NEW JERSEY | 1 | White Collar | 00 | 00xx-MISCELLANEOUS OCCUPATIONS | Administrative | 1 | General Schedule and Equivalently Graded (GSEG... | 11 | Standard GSEG Pay Plans | 2 | Non-permanent | 48-Excepted Service - Other | 1 | Full-time | Full-time Nonseasonal | 113.0 | 233 | 44956.060811 | -3159.060811 | 20.0 | 37.0 | 0.5 | 0.4 | 30 | 2.2 | 12 | 10 | 62 | 0.3 | 7 | 1.1 | 10.640580 | 0.894427 | 4.727388 | 5.451038 | 10.713441 |
| SEP | StratCount | |
|---|---|---|
| 0 | NS | 7498 |
| 1 | SA | 7501 |
| 2 | SB | 145 |
| 3 | SC | 7507 |
| 4 | SD | 7504 |
| 5 | SE | 555 |
| 6 | SF | 995 |
| 7 | SG | 414 |
| 8 | SH | 39 |
| 9 | SI | 1196 |
| 10 | SJ | 2771 |
| 11 | SK | 820 |
| 12 | SL | 11 |
CPU times: user 57.5 ms, sys: 17.1 ms, total: 74.6 ms Wall time: 49.5 ms
%%time
#### Analyze Missing Values
filtered_msnoData = msno.nullity_sort(msno.nullity_filter(SampledOPMDataAdmin, filter='bottom', n=15, p=0.999), sort='descending')
msno.matrix(filtered_msnoData)
del filtered_msnoData
/usr/local/es7/lib/python3.5/site-packages/matplotlib/axes/_base.py:2903: UserWarning: Attempting to set identical left==right results in singular transformations; automatically expanding. left=-0.5, right=-0.5 'left=%s, right=%s') % (left, right)) /usr/local/es7/lib/python3.5/site-packages/matplotlib/font_manager.py:1297: UserWarning: findfont: Font family ['sans-serif'] not found. Falling back to DejaVu Sans (prop.get_family(), self.defaultFamily[fontext]))
CPU times: user 351 ms, sys: 232 ms, total: 583 ms Wall time: 298 ms
%%time
## Describe Summary for our Model Professional Subgroup for Modeling
display(SampledOPMDataProf.describe().transpose())
| count | mean | std | min | 25% | 50% | 75% | max | |
|---|---|---|---|---|---|---|---|---|
| GSEGRD | 30227.0 | 12.142588 | 1.757041 | 7.000000 | 11.000000 | 12.000000 | 13.000000 | 15.000000 |
| SALARY | 30227.0 | 95587.756278 | 31018.478638 | 39179.000000 | 73143.000000 | 90711.000000 | 113673.000000 | 364782.000000 |
| LOS | 30227.0 | 14.333047 | 12.159974 | 0.000000 | 4.600000 | 9.700000 | 24.800000 | 71.500000 |
| SEPCount_EFDATE_OCC | 30227.0 | 146.208820 | 161.039507 | 1.000000 | 32.000000 | 82.000000 | 207.000000 | 708.000000 |
| SEPCount_EFDATE_LOC | 30227.0 | 737.081086 | 496.100839 | 18.000000 | 310.000000 | 596.000000 | 1123.000000 | 2791.000000 |
| IndAvgSalary | 30227.0 | 94981.435573 | 29622.413408 | 39179.000000 | 70499.260612 | 86256.027972 | 106119.623016 | 220806.750000 |
| SalaryOverUnderIndAvg | 30227.0 | 606.320705 | 9947.104620 | -119176.750000 | -5362.868080 | 227.033473 | 6789.125828 | 147896.670738 |
| LowerLimitAge | 30227.0 | 46.216462 | 12.931906 | 20.000000 | 35.000000 | 50.000000 | 60.000000 | 65.000000 |
| YearsToRetirement | 30227.0 | 10.783538 | 12.931906 | -8.000000 | -3.000000 | 7.000000 | 22.000000 | 37.000000 |
| BLS_FEDERAL_OtherSep_Rate | 30227.0 | 0.429315 | 0.084823 | 0.300000 | 0.400000 | 0.400000 | 0.500000 | 0.600000 |
| BLS_FEDERAL_Quits_Rate | 30227.0 | 0.455067 | 0.068862 | 0.300000 | 0.400000 | 0.500000 | 0.500000 | 0.600000 |
| BLS_FEDERAL_TotalSep_Level | 30227.0 | 36.292950 | 8.526718 | 26.000000 | 31.000000 | 34.000000 | 38.000000 | 60.000000 |
| BLS_FEDERAL_JobOpenings_Rate | 30227.0 | 2.450402 | 0.390278 | 1.900000 | 2.200000 | 2.300000 | 2.800000 | 3.200000 |
| BLS_FEDERAL_OtherSep_Level | 30227.0 | 11.951368 | 2.383434 | 8.000000 | 10.000000 | 12.000000 | 12.000000 | 17.000000 |
| BLS_FEDERAL_Quits_Level | 30227.0 | 12.004565 | 2.058074 | 9.000000 | 10.000000 | 13.000000 | 13.000000 | 17.000000 |
| BLS_FEDERAL_JobOpenings_Level | 30227.0 | 69.714196 | 11.375094 | 55.000000 | 62.000000 | 67.000000 | 80.000000 | 91.000000 |
| BLS_FEDERAL_Layoffs_Rate | 30227.0 | 0.461144 | 0.216841 | 0.300000 | 0.400000 | 0.400000 | 0.500000 | 1.100000 |
| BLS_FEDERAL_Layoffs_Level | 30227.0 | 12.412942 | 5.974603 | 7.000000 | 10.000000 | 12.000000 | 12.000000 | 30.000000 |
| BLS_FEDERAL_TotalSep_Rate | 30227.0 | 1.314652 | 0.316763 | 1.000000 | 1.100000 | 1.200000 | 1.400000 | 2.200000 |
| SALARYLog | 30227.0 | 11.417742 | 0.315717 | 10.575896 | 11.200172 | 11.415434 | 11.641081 | 12.807055 |
| LOSSqrt | 30227.0 | 3.392935 | 1.679622 | 0.000000 | 2.144761 | 3.114482 | 4.979960 | 8.455767 |
| SEPCount_EFDATE_OCCLog | 30227.0 | 4.297617 | 1.332665 | 0.000000 | 3.465736 | 4.406719 | 5.332719 | 6.562444 |
| SEPCount_EFDATE_LOCLog | 30227.0 | 6.319419 | 0.823629 | 2.890372 | 5.736572 | 6.390241 | 7.023759 | 7.934155 |
| IndAvgSalaryLog | 30227.0 | 11.416091 | 0.298912 | 10.575896 | 11.163358 | 11.365075 | 11.572322 | 12.305043 |
CPU times: user 99.1 ms, sys: 83.9 ms, total: 183 ms Wall time: 76.8 ms
#%%time
#OPMDataMerged.to_csv("OPMDataMerged.csv")
#os.path.getsize("OPMDataMerged.csv") #Display file size in bytes
Chris... can you use the SampledOPMDataProf dataset, and re-run the Visuals?
%%time
cols = list(SampledOPMDataProf.select_dtypes(include=['float64', 'int64']))
cols.remove('BLS_FEDERAL_OtherSep_Rate')
cols.remove('BLS_FEDERAL_Quits_Rate')
cols.remove('BLS_FEDERAL_TotalSep_Level')
cols.remove('BLS_FEDERAL_JobOpenings_Rate')
cols.remove('BLS_FEDERAL_OtherSep_Level')
cols.remove('BLS_FEDERAL_Quits_Level')
cols.remove('BLS_FEDERAL_JobOpenings_Level')
cols.remove('BLS_FEDERAL_Layoffs_Rate')
cols.remove('BLS_FEDERAL_Layoffs_Level')
cols.remove('BLS_FEDERAL_TotalSep_Rate')
cols.append('SEP')
display(cols)
plotNumeric = SampledOPMDataProf[cols]
# Create binary separation attribute for EDA correlation review
#plotNumeric["SEP_bin"] = plotNumeric.SEP.replace("NS", 1)
#plotNumeric.loc[plotNumeric['SEP_bin'] != 1, 'SEP_bin'] = 0
#plotNumeric.SEP_bin = plotNumeric.SEP_bin.apply(pd.to_numeric)
AttSplit = pd.get_dummies(plotNumeric['SEP'],prefix='SEP')
display(AttSplit.head())
plotNumeric = pd.concat((plotNumeric,AttSplit),axis=1) # add back into the dataframe
display(plotNumeric.head())
print("plotNumeric has {0} Records".format(len(plotNumeric)))
#print(plotNumeric.SEP_bin.dtype)
['GSEGRD', 'SALARY', 'LOS', 'SEPCount_EFDATE_OCC', 'SEPCount_EFDATE_LOC', 'IndAvgSalary', 'SalaryOverUnderIndAvg', 'LowerLimitAge', 'YearsToRetirement', 'SALARYLog', 'LOSSqrt', 'SEPCount_EFDATE_OCCLog', 'SEPCount_EFDATE_LOCLog', 'IndAvgSalaryLog', 'SEP']
| SEP_NS | SEP_SA | SEP_SB | SEP_SC | SEP_SD | SEP_SE | SEP_SF | SEP_SG | SEP_SH | SEP_SI | SEP_SJ | SEP_SK | SEP_SL | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
| 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
| 2 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
| 3 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
| 4 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
| GSEGRD | SALARY | LOS | SEPCount_EFDATE_OCC | SEPCount_EFDATE_LOC | IndAvgSalary | SalaryOverUnderIndAvg | LowerLimitAge | YearsToRetirement | SALARYLog | LOSSqrt | SEPCount_EFDATE_OCCLog | SEPCount_EFDATE_LOCLog | IndAvgSalaryLog | SEP | SEP_NS | SEP_SA | SEP_SB | SEP_SC | SEP_SD | SEP_SE | SEP_SF | SEP_SG | SEP_SH | SEP_SI | SEP_SJ | SEP_SK | SEP_SL | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | 11.0 | 77658.0 | 1.5 | 26.0 | 1133 | 78919.462629 | -1261.462629 | 20.0 | 37.0 | 11.260070 | 1.224745 | 3.258097 | 7.032624 | 11.276183 | NS | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
| 1 | 9.0 | 52146.0 | 2.9 | 274.0 | 1260 | 52460.545881 | -314.545881 | 20.0 | 37.0 | 10.861803 | 1.702939 | 5.613128 | 7.138867 | 10.867817 | NS | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
| 2 | 9.0 | 57368.0 | 0.5 | 7.0 | 555 | 58563.540541 | -1195.540541 | 20.0 | 37.0 | 10.957242 | 0.707107 | 1.945910 | 6.318968 | 10.977868 | NS | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
| 3 | 7.0 | 42830.0 | 1.5 | 82.0 | 304 | 43211.010135 | -381.010135 | 20.0 | 37.0 | 10.664994 | 1.224745 | 4.406719 | 5.717028 | 10.673851 | NS | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
| 4 | 7.0 | 44615.0 | 2.5 | 50.0 | 555 | 49854.574586 | -5239.574586 | 20.0 | 37.0 | 10.705825 | 1.581139 | 3.912023 | 6.318968 | 10.816866 | NS | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
plotNumeric has 30227 Records CPU times: user 38.2 ms, sys: 1.24 ms, total: 39.4 ms Wall time: 36.8 ms
%%time
sns.set(font_scale=1)
sns.pairplot(plotNumeric.drop(["SEP_NS",
"SEP_SA",
"SEP_SB",
"SEP_SC",
"SEP_SD",
"SEP_SE",
"SEP_SF",
"SEP_SG",
"SEP_SH",
"SEP_SI",
"SEP_SJ",
"SEP_SK",
"SEP_SL"
], axis=1), hue = 'SEP', palette="hls", plot_kws={"s": 50})
/usr/local/es7/lib/python3.5/site-packages/matplotlib/font_manager.py:1297: UserWarning: findfont: Font family ['sans-serif'] not found. Falling back to DejaVu Sans (prop.get_family(), self.defaultFamily[fontext]))
CPU times: user 1min 32s, sys: 1min 29s, total: 3min 1s Wall time: 1min 14s
%%time
# Function modified from https://stackoverflow.com/questions/29530355/plotting-multiple-histograms-in-grid
sns.set()
def draw_histograms(df, variables, n_rows, n_cols):
fig=plt.figure(figsize=(20,20))
for i, var_name in enumerate(variables):
ax=fig.add_subplot(n_rows,n_cols,i+1)
df[var_name].hist(bins=20,ax=ax, color='#58D68D')
ax.set_title(var_name+" Distribution")
fig.tight_layout() # Improves appearance a bit.
plt.show()
draw_histograms(plotNumeric.drop(['SEP',
"SEP_NS",
"SEP_SA",
"SEP_SB",
"SEP_SC",
"SEP_SD",
"SEP_SE",
"SEP_SF",
"SEP_SG",
"SEP_SH",
"SEP_SI",
"SEP_SJ",
"SEP_SK",
"SEP_SL"
], axis=1),
plotNumeric.drop(['SEP',
"SEP_NS",
"SEP_SA",
"SEP_SB",
"SEP_SC",
"SEP_SD",
"SEP_SE",
"SEP_SF",
"SEP_SG",
"SEP_SH",
"SEP_SI",
"SEP_SJ",
"SEP_SK",
"SEP_SL"
], axis=1).columns, 6, 3)
/usr/local/es7/lib/python3.5/site-packages/matplotlib/font_manager.py:1297: UserWarning: findfont: Font family ['sans-serif'] not found. Falling back to DejaVu Sans (prop.get_family(), self.defaultFamily[fontext]))
CPU times: user 3.45 s, sys: 2.77 s, total: 6.22 s Wall time: 2.88 s
%%time
# Inspired by http://seaborn.pydata.org/examples/many_pairwise_correlations.html
#plt.matshow(plotNumeric.corr())
sns.set(style='white')
corr = plotNumeric.drop(['SEP'], axis=1).corr()
# Generate a mask for the upper triangle
mask = np.zeros_like(corr, dtype=np.bool)
mask[np.triu_indices_from(mask, k=1)] = True
# Set up the matplotlib figure
f, ax = plt.subplots(figsize=(20, 20))
# Generate a custom diverging colormap
cmap = sns.diverging_palette(250, 10, as_cmap=True)
# Draw the heatmap with the mask and correct aspect ratio
sns.set(font_scale=0.95)
heatCorr = sns.heatmap(corr, mask=mask, cmap=cmap, vmax=1, vmin=-1,
square=True, annot=True, linewidths=1,
cbar_kws={"shrink": .5}, ax=ax, fmt='.1g')
#heatCorr.
ax.tick_params(labelsize=15)
cax = plt.gcf().axes[-1]
cax.tick_params(labelsize=15)
sns.plt.show()
#sns.heatmap(corr, annot=True, linewidths=0.01, cmap=cmap, ax=ax)
/usr/local/es7/lib/python3.5/site-packages/matplotlib/font_manager.py:1297: UserWarning: findfont: Font family ['sans-serif'] not found. Falling back to DejaVu Sans (prop.get_family(), self.defaultFamily[fontext]))
CPU times: user 3.37 s, sys: 925 ms, total: 4.29 s Wall time: 3.24 s
%%time
cols = list(SampledOPMDataProf.select_dtypes(include=['object']))
dropCols = ["LOCTYP",
"LOCTYPT",
"OCCTYP",
"OCCTYPT",
"PPTYP",
"PPTYPT",
"AGYTYP",
"OCCFAM",
"PPGROUP",
"PAYPLAN",
"TOATYP",
"WSTYP",
"AGYSUBT",
"AGELVL",
"LOSLVL",
"LOC",
"OCC",
"PATCO",
"SALLVL",
"TOA",
"WORKSCH"]
for i in dropCols:
if(i in list(SampledOPMDataProf.columns)): cols.remove(i)
plotCat = SampledOPMDataProf[cols]
display(plotCat.head())
print("plotCat Has {0} Records".format(len(plotCat)))
print("Number of colums = ", len(cols))
| SEP | DATECODE | AGELVLT | LOCT | OCCFAMT | PATCOT | PPGROUPT | TOATYPT | TOAT | WSTYPT | WORKSCHT | |
|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | NS | 201412 | 20-24 | 51-VIRGINIA | 12xx-COPYRIGHT, PATENT, AND TRADE-MARK | Professional | Standard GSEG Pay Plans | Permanent | 15-Competitive Service - Career-Conditional | Full-time | Full-time Nonseasonal |
| 1 | NS | 201412 | 20-24 | 11-DISTRICT OF COLUMBIA | 11xx-BUSINESS AND INDUSTRY | Professional | Standard GSEG Pay Plans | Permanent | 15-Competitive Service - Career-Conditional | Full-time | Full-time Nonseasonal |
| 2 | NS | 201412 | 20-24 | 53-WASHINGTON | 08xx-ENGINEERING AND ARCHITECTURE | Professional | Standard GSEG Pay Plans | Permanent | 15-Competitive Service - Career-Conditional | Full-time | Full-time Nonseasonal |
| 3 | NS | 201412 | 20-24 | 25-MASSACHUSETTS | 05xx-ACCOUNTING AND BUDGET | Professional | Standard GSEG Pay Plans | Permanent | 15-Competitive Service - Career-Conditional | Full-time | Full-time Nonseasonal |
| 4 | NS | 201412 | 20-24 | 53-WASHINGTON | 08xx-ENGINEERING AND ARCHITECTURE | Professional | Standard GSEG Pay Plans | Permanent | 15-Competitive Service - Career-Conditional | Full-time | Full-time Nonseasonal |
plotCat Has 30227 Records Number of colums = 11 CPU times: user 19.7 ms, sys: 2.93 ms, total: 22.7 ms Wall time: 20.6 ms
%%time
for i in cols:
if i != 'SEP':
plt.figure(i) # Required to create new figure each loop rather than drawing over previous object
f, (ax1, ax2) = plt.subplots(ncols=2, figsize=(20, 10), sharey=False)
sns.countplot(y=i, data=plotCat, color="lightblue", ax=ax1);
sns.countplot(y=i, data=plotCat, hue="SEP", palette="hls", ax=ax2);
if i == 'AGYSUB':
subCountPlot(i, 'SEP', 10000)
elif i == 'LOCT':
subCountPlot(i, 'SEP', 1000)
elif i == 'OCCT':
subCountPlot(i, 'SEP', 2000)
elif i == 'PPGRD':
subCountPlot(i, 'SEP', 6000)
elif i == 'AGYT':
subCountPlot(i, 'SEP', 3000)
CPU times: user 5.05 s, sys: 30 ms, total: 5.08 s Wall time: 5.05 s
/usr/local/es7/lib/python3.5/site-packages/matplotlib/pyplot.py:524: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`). max_open_warning, RuntimeWarning)
<matplotlib.figure.Figure at 0x7f71b4d40978>
/usr/local/es7/lib/python3.5/site-packages/matplotlib/font_manager.py:1297: UserWarning: findfont: Font family ['sans-serif'] not found. Falling back to DejaVu Sans (prop.get_family(), self.defaultFamily[fontext]))
<matplotlib.figure.Figure at 0x7f7198b20518>
<matplotlib.figure.Figure at 0x7f720c416320>
<matplotlib.figure.Figure at 0x7f71a8343630>
<matplotlib.figure.Figure at 0x7f719cc031d0>
<matplotlib.figure.Figure at 0x7f719ea51dd8>
<matplotlib.figure.Figure at 0x7f719dc570b8>
<matplotlib.figure.Figure at 0x7f722c35e0b8>
<matplotlib.figure.Figure at 0x7f719bb6dc50>
<matplotlib.figure.Figure at 0x7f719bc40780>
%%time
for i in cols:
if i != 'SEP':
percBarPlot(i, 'SEP', len(plotCat.SEP.drop_duplicates()))
CPU times: user 2.47 s, sys: 34.9 ms, total: 2.5 s Wall time: 2.48 s
/usr/local/es7/lib/python3.5/site-packages/matplotlib/font_manager.py:1297: UserWarning: findfont: Font family ['sans-serif'] not found. Falling back to DejaVu Sans (prop.get_family(), self.defaultFamily[fontext]))
%%time
sns.set(style="whitegrid", palette="pastel", color_codes=True)
sns.violinplot(x="PATCOT", y="SALARY", data=SampledOPMDataProf, split=True,
inner="quart")
sns.despine(left=True)
CPU times: user 1.66 s, sys: 8.85 s, total: 10.5 s Wall time: 254 ms
/usr/local/es7/lib/python3.5/site-packages/matplotlib/font_manager.py:1297: UserWarning: findfont: Font family ['sans-serif'] not found. Falling back to DejaVu Sans (prop.get_family(), self.defaultFamily[fontext]))
%%time
# Draw a nested violinplot and split the violins for easier comparison
sns.violinplot(x="SEP", y="SALARY", data=SampledOPMDataProf, split=True,
inner="box", scale="area", cut=0)
sns.despine(left=True)
CPU times: user 341 ms, sys: 248 ms, total: 589 ms Wall time: 295 ms
/usr/local/es7/lib/python3.5/site-packages/matplotlib/font_manager.py:1297: UserWarning: findfont: Font family ['sans-serif'] not found. Falling back to DejaVu Sans (prop.get_family(), self.defaultFamily[fontext]))
#%%time
#
#sns.factorplot(x="SEP", y="SALARY", col="PATCOT",
# data=SampledOPMDataProf,
# kind="violin", split=True, aspect=.5, size=15);
#%%time
#
#sns.factorplot(x="SEP", y="SALARY", col="PATCOT", data=SampledOPMDataProf,
# kind="violin", split=True, aspect=.4, size=10);
%%time
g = sns.PairGrid(data=SampledOPMDataProf,
x_vars=["SEP","PATCOT"],
y_vars=["SALARY", "LOS", "LowerLimitAge", "YearsToRetirement"],
aspect=1, size=10)
g.map(sns.violinplot, palette="pastel", inner="quart");
/usr/local/es7/lib/python3.5/site-packages/matplotlib/font_manager.py:1297: UserWarning: findfont: Font family ['sans-serif'] not found. Falling back to DejaVu Sans (prop.get_family(), self.defaultFamily[fontext]))
CPU times: user 7.67 s, sys: 33.2 s, total: 40.9 s Wall time: 2.52 s
There are several separation types we would like to either roll up, or remove altogether.
We have chosen to roll separation into two Binary Categories.
1) NS, Non-Separation comprised of: a) NS, Non-Separation b) SA,Transfer Out - Individual Transfer c) SB,Transfer Out - Mass Transfer d) SD,Retirement - Voluntary e) SE,Retirement - Early Out f) SF,Retirement - Disability g) SG,Retirement - Other
2) SC, Quit
SampledOPMDataProf = SampledOPMDataProf[((SampledOPMDataProf["SEP"] == "NS") | (SampledOPMDataProf["SEP"] == "SD") | (SampledOPMDataProf["SEP"] == "SE") | (SampledOPMDataProf["SEP"] == "SF") | (SampledOPMDataProf["SEP"] == "SH") | (SampledOPMDataProf["SEP"] == "SA") | (SampledOPMDataProf["SEP"] == "SB") | (SampledOPMDataProf["SEP"] == "SC"))]
SampledOPMDataProf.loc[(SampledOPMDataProf["SEP"] != "SC") , "SEP"]="NS"
SampledOPMDataAdmin = SampledOPMDataAdmin[((SampledOPMDataAdmin["SEP"] == "NS") | (SampledOPMDataAdmin["SEP"] == "SD") | (SampledOPMDataAdmin["SEP"] == "SE") | (SampledOPMDataAdmin["SEP"] == "SF") | (SampledOPMDataAdmin["SEP"] == "SH") | (SampledOPMDataAdmin["SEP"] == "SA") | (SampledOPMDataAdmin["SEP"] == "SB") | (SampledOPMDataAdmin["SEP"] == "SC"))]
SampledOPMDataAdmin.loc[(SampledOPMDataAdmin["SEP"] != "SC") , "SEP"]="NS"
#Assess Stratum SEP Counts for Prof, for use in sampling
maxSize=7500
stratumProf = pd.DataFrame({'StratCount' : SampledOPMDataProf.groupby(["SEP"]).size()}).reset_index()
stratumProf.loc[stratumProf["StratCount"]>maxSize,"StratCountSample"] = maxSize
stratumProf.loc[stratumProf["StratCount"]<=maxSize,"StratCountSample"] = stratumProf["StratCount"]
#else: stratum["StratCountSample"] = stratum["StratCount"]
display(stratumProf)
| SEP | StratCount | StratCountSample | |
|---|---|---|---|
| 0 | NS | 21076 | 7500.0 |
| 1 | SC | 7423 | 7423.0 |
#Assess Stratum SEP Counts for Admin, for use in sampling
maxSize=7500
stratumAdmin = pd.DataFrame({'StratCount' : SampledOPMDataAdmin.groupby(["SEP"]).size()}).reset_index()
stratumAdmin.loc[stratumAdmin["StratCount"]>maxSize,"StratCountSample"] = maxSize
stratumAdmin.loc[stratumAdmin["StratCount"]<=maxSize,"StratCountSample"] = stratumAdmin["StratCount"]
#else: stratum["StratCountSample"] = stratum["StratCount"]
display(stratumAdmin)
| SEP | StratCount | StratCountSample | |
|---|---|---|---|
| 0 | NS | 24237 | 7500.0 |
| 1 | SC | 7507 | 7500.0 |
SampledOPMDataProf= SampleStrata(stratumProf, SampledOPMDataProf, "SampledOPMDataProfBinary")
SampledOPMDataAdmin= SampleStrata(stratumProf, SampledOPMDataAdmin, "SampledOPMDataProfBinary")
Stratum Sample Size Calculations for SEP: NS
| DATECODE | AGELVL | StratCount | SEP | TotalCount | p | StratCountSample | StratSampleSize | |
|---|---|---|---|---|---|---|---|---|
| 0 | 201410 | B | 2 | NS | 21076 | 0.000095 | 7500.0 | 1 |
| 1 | 201410 | C | 57 | NS | 21076 | 0.002704 | 7500.0 | 20 |
| 2 | 201410 | D | 85 | NS | 21076 | 0.004033 | 7500.0 | 30 |
| 3 | 201410 | E | 73 | NS | 21076 | 0.003464 | 7500.0 | 26 |
| 4 | 201410 | F | 74 | NS | 21076 | 0.003511 | 7500.0 | 26 |
totalStratumSampleSize: 7497 Stratum Sample Size Calculations for SEP: SC
| DATECODE | AGELVL | StratCount | SEP | TotalCount | p | StratCountSample | StratSampleSize | |
|---|---|---|---|---|---|---|---|---|
| 0 | 201410 | B | 10 | SC | 7423 | 0.001347 | 7423.0 | 10 |
| 1 | 201410 | C | 92 | SC | 7423 | 0.012394 | 7423.0 | 92 |
| 2 | 201410 | D | 154 | SC | 7423 | 0.020746 | 7423.0 | 154 |
| 3 | 201410 | E | 118 | SC | 7423 | 0.015897 | 7423.0 | 118 |
| 4 | 201410 | F | 80 | SC | 7423 | 0.010777 | 7423.0 | 80 |
totalStratumSampleSize: 7423 NS 201410 B 1 NS 201410 C 20 NS 201410 D 30 NS 201410 E 26 NS 201410 F 26 NS 201410 G 27 NS 201410 H 25 NS 201410 I 66 NS 201410 J 77 NS 201410 K 68 NS 201411 B 1 NS 201411 C 25 NS 201411 D 42 NS 201411 E 35 NS 201411 F 34 NS 201411 G 26 NS 201411 H 34 NS 201411 I 59 NS 201411 J 56 NS 201411 K 42 NS 201412 B 5 NS 201412 C 53 NS 201412 D 99 NS 201412 E 95 NS 201412 F 90 NS 201412 G 99 NS 201412 H 134 NS 201412 I 192 NS 201412 J 200 NS 201412 K 169 NS 201501 B 1 NS 201501 C 12 NS 201501 D 27 NS 201501 E 30 NS 201501 F 19 NS 201501 G 26 NS 201501 H 32 NS 201501 I 182 NS 201501 J 218 NS 201501 K 190 NS 201502 B 1 NS 201502 C 16 NS 201502 D 23 NS 201502 E 18 NS 201502 F 17 NS 201502 G 16 NS 201502 H 27 NS 201502 I 51 NS 201502 J 67 NS 201502 K 49 NS 201503 B 6 NS 201503 C 57 NS 201503 D 117 NS 201503 E 108 NS 201503 F 105 NS 201503 G 118 NS 201503 H 140 NS 201503 I 142 NS 201503 J 133 NS 201503 K 81 NS 201504 C 14 NS 201504 D 27 NS 201504 E 29 NS 201504 F 25 NS 201504 G 20 NS 201504 H 31 NS 201504 I 54 NS 201504 J 67 NS 201504 K 64 NS 201505 B 1 NS 201505 C 23 NS 201505 D 41 NS 201505 E 39 NS 201505 F 35 NS 201505 G 37 NS 201505 H 40 NS 201505 I 105 NS 201505 J 112 NS 201505 K 93 NS 201506 B 8 NS 201506 C 59 NS 201506 D 114 NS 201506 E 107 NS 201506 F 96 NS 201506 G 102 NS 201506 H 137 NS 201506 I 159 NS 201506 J 149 NS 201506 K 94 NS 201507 B 1 NS 201507 C 18 NS 201507 D 29 NS 201507 E 33 NS 201507 F 23 NS 201507 G 21 NS 201507 H 28 NS 201507 I 80 NS 201507 J 89 NS 201507 K 78 NS 201508 B 0 NS 201508 C 18 NS 201508 D 34 NS 201508 E 22 NS 201508 F 21 NS 201508 G 22 NS 201508 H 30 NS 201508 I 54 NS 201508 J 70 NS 201508 K 50 NS 201509 B 7 NS 201509 C 57 NS 201509 D 121 NS 201509 E 111 NS 201509 F 99 NS 201509 G 110 NS 201509 H 134 NS 201509 I 151 NS 201509 J 130 NS 201509 K 89 SC 201410 B 10 SC 201410 C 92 SC 201410 D 154 SC 201410 E 118 SC 201410 F 80 SC 201410 G 61 SC 201410 H 64 SC 201410 I 48 SC 201410 J 24 SC 201410 K 11 SC 201411 B 6 SC 201411 C 72 SC 201411 D 90 SC 201411 E 85 SC 201411 F 70 SC 201411 G 55 SC 201411 H 61 SC 201411 I 40 SC 201411 J 17 SC 201411 K 7 SC 201412 B 3 SC 201412 C 63 SC 201412 D 103 SC 201412 E 81 SC 201412 F 66 SC 201412 G 45 SC 201412 H 47 SC 201412 I 43 SC 201412 J 12 SC 201412 K 14 SC 201501 B 4 SC 201501 C 106 SC 201501 D 132 SC 201501 E 122 SC 201501 F 95 SC 201501 G 71 SC 201501 H 72 SC 201501 I 42 SC 201501 J 23 SC 201501 K 9 SC 201502 B 6 SC 201502 C 63 SC 201502 D 98 SC 201502 E 79 SC 201502 F 71 SC 201502 G 49 SC 201502 H 50 SC 201502 I 44 SC 201502 J 16 SC 201502 K 12 SC 201503 B 9 SC 201503 C 60 SC 201503 D 101 SC 201503 E 91 SC 201503 F 52 SC 201503 G 56 SC 201503 H 38 SC 201503 I 38 SC 201503 J 21 SC 201503 K 11 SC 201504 B 11 SC 201504 C 86 SC 201504 D 104 SC 201504 E 72 SC 201504 F 58 SC 201504 G 58 SC 201504 H 56 SC 201504 I 36 SC 201504 J 13 SC 201504 K 12 SC 201505 B 19 SC 201505 C 140 SC 201505 D 162 SC 201505 E 105 SC 201505 F 105 SC 201505 G 72 SC 201505 H 66 SC 201505 I 56 SC 201505 J 28 SC 201505 K 11 SC 201506 B 16 SC 201506 C 109 SC 201506 D 139 SC 201506 E 126 SC 201506 F 84 SC 201506 G 76 SC 201506 H 68 SC 201506 I 45 SC 201506 J 21 SC 201506 K 16 SC 201507 B 24 SC 201507 C 139 SC 201507 D 141 SC 201507 E 120 SC 201507 F 103 SC 201507 G 71 SC 201507 H 57 SC 201507 I 56 SC 201507 J 29 SC 201507 K 15 SC 201508 B 17 SC 201508 C 105 SC 201508 D 164 SC 201508 E 140 SC 201508 F 91 SC 201508 G 82 SC 201508 H 65 SC 201508 I 48 SC 201508 J 15 SC 201508 K 6 SC 201509 B 12 SC 201509 C 102 SC 201509 D 160 SC 201509 E 97 SC 201509 F 87 SC 201509 G 69 SC 201509 H 66 SC 201509 I 45 SC 201509 J 29 SC 201509 K 15 Stratum Sample Size Calculations for SEP: NS
| DATECODE | AGELVL | StratCount | SEP | TotalCount | p | StratCountSample | StratSampleSize | |
|---|---|---|---|---|---|---|---|---|
| 0 | 201410 | B | 3 | NS | 24237 | 0.000124 | 7500.0 | 1 |
| 1 | 201410 | C | 57 | NS | 24237 | 0.002352 | 7500.0 | 18 |
| 2 | 201410 | D | 133 | NS | 24237 | 0.005487 | 7500.0 | 41 |
| 3 | 201410 | E | 98 | NS | 24237 | 0.004043 | 7500.0 | 30 |
| 4 | 201410 | F | 114 | NS | 24237 | 0.004704 | 7500.0 | 35 |
totalStratumSampleSize: 7495 Stratum Sample Size Calculations for SEP: SC
| DATECODE | AGELVL | StratCount | SEP | TotalCount | p | StratCountSample | StratSampleSize | |
|---|---|---|---|---|---|---|---|---|
| 0 | 201410 | B | 16 | SC | 7507 | 0.002131 | 7423.0 | 16 |
| 1 | 201410 | C | 77 | SC | 7507 | 0.010257 | 7423.0 | 76 |
| 2 | 201410 | D | 142 | SC | 7507 | 0.018916 | 7423.0 | 140 |
| 3 | 201410 | E | 110 | SC | 7507 | 0.014653 | 7423.0 | 109 |
| 4 | 201410 | F | 66 | SC | 7507 | 0.008792 | 7423.0 | 65 |
totalStratumSampleSize: 7423 NS 201410 B 1 NS 201410 C 18 NS 201410 D 41 NS 201410 E 30 NS 201410 F 35 NS 201410 G 42 NS 201410 H 53 NS 201410 I 75 NS 201410 J 66 NS 201410 K 36 NS 201411 B 2 NS 201411 C 19 NS 201411 D 42 NS 201411 E 39 NS 201411 F 46 NS 201411 G 45 NS 201411 H 52 NS 201411 I 70 NS 201411 J 48 NS 201411 K 31 NS 201412 B 2 NS 201412 C 32 NS 201412 D 71 NS 201412 E 79 NS 201412 F 86 NS 201412 G 127 NS 201412 H 174 NS 201412 I 207 NS 201412 J 189 NS 201412 K 128 NS 201501 B 1 NS 201501 C 14 NS 201501 D 24 NS 201501 E 30 NS 201501 F 35 NS 201501 G 35 NS 201501 H 51 NS 201501 I 189 NS 201501 J 191 NS 201501 K 121 NS 201502 B 2 NS 201502 C 18 NS 201502 D 28 NS 201502 E 25 NS 201502 F 27 NS 201502 G 36 NS 201502 H 37 NS 201502 I 58 NS 201502 J 52 NS 201502 K 30 NS 201503 B 2 NS 201503 C 38 NS 201503 D 88 NS 201503 E 92 NS 201503 F 91 NS 201503 G 132 NS 201503 H 173 NS 201503 I 156 NS 201503 J 110 NS 201503 K 61 NS 201504 B 1 NS 201504 C 13 NS 201504 D 30 NS 201504 E 28 NS 201504 F 33 NS 201504 G 31 NS 201504 H 45 NS 201504 I 71 NS 201504 J 61 NS 201504 K 39 NS 201505 B 0 NS 201505 C 21 NS 201505 D 50 NS 201505 E 43 NS 201505 F 40 NS 201505 G 47 NS 201505 H 69 NS 201505 I 112 NS 201505 J 93 NS 201505 K 63 NS 201506 B 2 NS 201506 C 35 NS 201506 D 79 NS 201506 E 86 NS 201506 F 90 NS 201506 G 125 NS 201506 H 163 NS 201506 I 179 NS 201506 J 121 NS 201506 K 68 NS 201507 B 0 NS 201507 C 12 NS 201507 D 42 NS 201507 E 31 NS 201507 F 33 NS 201507 G 42 NS 201507 H 67 NS 201507 I 101 NS 201507 J 85 NS 201507 K 50 NS 201508 B 1 NS 201508 C 11 NS 201508 D 33 NS 201508 E 33 NS 201508 F 26 NS 201508 G 40 NS 201508 H 53 NS 201508 I 67 NS 201508 J 56 NS 201508 K 33 NS 201509 B 3 NS 201509 C 40 NS 201509 D 89 NS 201509 E 103 NS 201509 F 98 NS 201509 G 138 NS 201509 H 176 NS 201509 I 176 NS 201509 J 126 NS 201509 K 59 SC 201410 B 16 SC 201410 C 76 SC 201410 D 140 SC 201410 E 109 SC 201410 F 65 SC 201410 G 91 SC 201410 H 88 SC 201410 I 46 SC 201410 J 25 SC 201410 K 8 SC 201411 B 7 SC 201411 C 66 SC 201411 D 94 SC 201411 E 90 SC 201411 F 80 SC 201411 G 75 SC 201411 H 70 SC 201411 I 38 SC 201411 J 11 SC 201411 K 7 SC 201412 B 2 SC 201412 C 59 SC 201412 D 92 SC 201412 E 67 SC 201412 F 56 SC 201412 G 61 SC 201412 H 65 SC 201412 I 39 SC 201412 J 18 SC 201412 K 6 SC 201501 B 5 SC 201501 C 60 SC 201501 D 123 SC 201501 E 114 SC 201501 F 96 SC 201501 G 79 SC 201501 H 78 SC 201501 I 58 SC 201501 J 21 SC 201501 K 6 SC 201502 B 11 SC 201502 C 60 SC 201502 D 105 SC 201502 E 73 SC 201502 F 72 SC 201502 G 69 SC 201502 H 74 SC 201502 I 47 SC 201502 J 17 SC 201502 K 6 SC 201503 B 6 SC 201503 C 72 SC 201503 D 114 SC 201503 E 96 SC 201503 F 52 SC 201503 G 69 SC 201503 H 86 SC 201503 I 47 SC 201503 J 17 SC 201503 K 7 SC 201504 B 11 SC 201504 C 65 SC 201504 D 127 SC 201504 E 85 SC 201504 F 92 SC 201504 G 71 SC 201504 H 77 SC 201504 I 38 SC 201504 J 16 SC 201504 K 3 SC 201505 B 20 SC 201505 C 87 SC 201505 D 142 SC 201505 E 124 SC 201505 F 89 SC 201505 G 87 SC 201505 H 92 SC 201505 I 57 SC 201505 J 28 SC 201505 K 13 SC 201506 B 22 SC 201506 C 69 SC 201506 D 117 SC 201506 E 97 SC 201506 F 92 SC 201506 G 75 SC 201506 H 64 SC 201506 I 49 SC 201506 J 12 SC 201506 K 9 SC 201507 B 20 SC 201507 C 97 SC 201507 D 141 SC 201507 E 119 SC 201507 F 110 SC 201507 G 96 SC 201507 H 82 SC 201507 I 37 SC 201507 J 21 SC 201507 K 7 SC 201508 B 25 SC 201508 C 100 SC 201508 D 135 SC 201508 E 112 SC 201508 F 92 SC 201508 G 93 SC 201508 H 74 SC 201508 I 44 SC 201508 J 15 SC 201508 K 5 SC 201509 B 11 SC 201509 C 65 SC 201509 D 128 SC 201509 E 117 SC 201509 F 100 SC 201509 G 94 SC 201509 H 73 SC 201509 I 48 SC 201509 J 16 SC 201509 K 9
%%time
cols = list(SampledOPMDataProf.select_dtypes(include=['float64', 'int64']))
cols.remove('BLS_FEDERAL_OtherSep_Rate')
cols.remove('BLS_FEDERAL_Quits_Rate')
cols.remove('BLS_FEDERAL_TotalSep_Level')
cols.remove('BLS_FEDERAL_JobOpenings_Rate')
cols.remove('BLS_FEDERAL_OtherSep_Level')
cols.remove('BLS_FEDERAL_Quits_Level')
cols.remove('BLS_FEDERAL_JobOpenings_Level')
cols.remove('BLS_FEDERAL_Layoffs_Rate')
cols.remove('BLS_FEDERAL_Layoffs_Level')
cols.remove('BLS_FEDERAL_TotalSep_Rate')
cols.append('SEP')
display(cols)
plotNumeric = SampledOPMDataProf[cols]
# Create binary separation attribute for EDA correlation review
#plotNumeric["SEP_bin"] = plotNumeric.SEP.replace("NS", 1)
#plotNumeric.loc[plotNumeric['SEP_bin'] != 1, 'SEP_bin'] = 0
#plotNumeric.SEP_bin = plotNumeric.SEP_bin.apply(pd.to_numeric)
AttSplit = pd.get_dummies(plotNumeric['SEP'],prefix='SEP')
display(AttSplit.head())
plotNumeric = pd.concat((plotNumeric,AttSplit),axis=1) # add back into the dataframe
display(plotNumeric.head())
print("plotNumeric has {0} Records".format(len(plotNumeric)))
#print(plotNumeric.SEP_bin.dtype)
['GSEGRD', 'SALARY', 'LOS', 'SEPCount_EFDATE_OCC', 'SEPCount_EFDATE_LOC', 'IndAvgSalary', 'SalaryOverUnderIndAvg', 'LowerLimitAge', 'YearsToRetirement', 'SALARYLog', 'LOSSqrt', 'SEPCount_EFDATE_OCCLog', 'SEPCount_EFDATE_LOCLog', 'IndAvgSalaryLog', 'SEP']
| SEP_NS | SEP_SC | |
|---|---|---|
| 0 | 1 | 0 |
| 1 | 1 | 0 |
| 2 | 1 | 0 |
| 3 | 1 | 0 |
| 4 | 1 | 0 |
| GSEGRD | SALARY | LOS | SEPCount_EFDATE_OCC | SEPCount_EFDATE_LOC | IndAvgSalary | SalaryOverUnderIndAvg | LowerLimitAge | YearsToRetirement | SALARYLog | LOSSqrt | SEPCount_EFDATE_OCCLog | SEPCount_EFDATE_LOCLog | IndAvgSalaryLog | SEP | SEP_NS | SEP_SC | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | 11.0 | 61857.0 | 4.7 | 336.0 | 470 | 65898.205859 | -4041.205859 | 20.0 | 37.0 | 11.032581 | 2.167948 | 5.817111 | 6.152733 | 11.095866 | NS | 1 | 0 |
| 1 | 12.0 | 71813.0 | 7.2 | 336.0 | 513 | 81218.917413 | -9405.917413 | 25.0 | 32.0 | 11.181821 | 2.683282 | 5.817111 | 6.240276 | 11.304903 | NS | 1 | 0 |
| 2 | 11.0 | 63091.0 | 4.0 | 336.0 | 923 | 65898.205859 | -2807.205859 | 25.0 | 32.0 | 11.052333 | 2.000000 | 5.817111 | 6.827629 | 11.095866 | NS | 1 | 0 |
| 3 | 12.0 | 75621.0 | 5.8 | 75.0 | 923 | 82168.243394 | -6547.243394 | 25.0 | 32.0 | 11.233489 | 2.408319 | 4.317488 | 6.827629 | 11.316524 | NS | 1 | 0 |
| 4 | 13.0 | 95919.0 | 2.0 | 63.0 | 923 | 121938.733696 | -26019.733696 | 25.0 | 32.0 | 11.471259 | 1.414214 | 4.143135 | 6.827629 | 11.711274 | NS | 1 | 0 |
plotNumeric has 14920 Records CPU times: user 37.1 ms, sys: 1.19 ms, total: 38.3 ms Wall time: 36.3 ms
%%time
sns.set(font_scale=1)
sns.pairplot(plotNumeric.drop(['SEP_NS',
'SEP_SC'], axis=1), hue = 'SEP', palette="hls", plot_kws={"s": 50})
/usr/local/es7/lib/python3.5/site-packages/matplotlib/font_manager.py:1297: UserWarning: findfont: Font family ['sans-serif'] not found. Falling back to DejaVu Sans (prop.get_family(), self.defaultFamily[fontext]))
CPU times: user 45.7 s, sys: 41.6 s, total: 1min 27s Wall time: 37.1 s
%%time
# Function modified from https://stackoverflow.com/questions/29530355/plotting-multiple-histograms-in-grid
sns.set()
def draw_histograms(df, variables, n_rows, n_cols):
fig=plt.figure(figsize=(20,20))
for i, var_name in enumerate(variables):
ax=fig.add_subplot(n_rows,n_cols,i+1)
df[var_name].hist(bins=20,ax=ax, color='#58D68D')
ax.set_title(var_name+" Distribution")
fig.tight_layout() # Improves appearance a bit.
plt.show()
draw_histograms(plotNumeric.drop(['SEP',
'SEP_NS',
'SEP_SC'
], axis=1),
plotNumeric.drop(['SEP',
'SEP_NS',
'SEP_SC'
], axis=1).columns, 6, 3)
/usr/local/es7/lib/python3.5/site-packages/matplotlib/font_manager.py:1297: UserWarning: findfont: Font family ['sans-serif'] not found. Falling back to DejaVu Sans (prop.get_family(), self.defaultFamily[fontext]))
CPU times: user 4.15 s, sys: 2.77 s, total: 6.91 s Wall time: 3.64 s
%%time
# Inspired by http://seaborn.pydata.org/examples/many_pairwise_correlations.html
#plt.matshow(plotNumeric.corr())
sns.set(style='white')
corr = plotNumeric.drop(['SEP'], axis=1).corr()
# Generate a mask for the upper triangle
mask = np.zeros_like(corr, dtype=np.bool)
mask[np.triu_indices_from(mask, k=1)] = True
# Set up the matplotlib figure
f, ax = plt.subplots(figsize=(20, 20))
# Generate a custom diverging colormap
cmap = sns.diverging_palette(250, 10, as_cmap=True)
# Draw the heatmap with the mask and correct aspect ratio
sns.set(font_scale=0.95)
heatCorr = sns.heatmap(corr, mask=mask, cmap=cmap, vmax=1, vmin=-1,
square=True, annot=True, linewidths=1,
cbar_kws={"shrink": .5}, ax=ax, fmt='.1g')
#heatCorr.
ax.tick_params(labelsize=15)
cax = plt.gcf().axes[-1]
cax.tick_params(labelsize=15)
sns.plt.show()
#sns.heatmap(corr, annot=True, linewidths=0.01, cmap=cmap, ax=ax)
/usr/local/es7/lib/python3.5/site-packages/matplotlib/font_manager.py:1297: UserWarning: findfont: Font family ['sans-serif'] not found. Falling back to DejaVu Sans (prop.get_family(), self.defaultFamily[fontext]))
CPU times: user 1.64 s, sys: 870 ms, total: 2.51 s Wall time: 1.49 s
%%time
cols = list(SampledOPMDataProf.select_dtypes(include=['object']))
dropCols = ["LOCTYP",
"LOCTYPT",
"OCCTYP",
"OCCTYPT",
"PPTYP",
"PPTYPT",
"AGYTYP",
"OCCFAM",
"PPGROUP",
"PAYPLAN",
"TOATYP",
"WSTYP",
"AGYSUBT",
"AGELVL",
"LOSLVL",
"LOC",
"OCC",
"PATCO",
"SALLVL",
"TOA",
"WORKSCH"]
for i in dropCols:
if(i in list(SampledOPMDataProf.columns)): cols.remove(i)
plotCat = SampledOPMDataProf[cols]
display(plotCat.head())
print("plotCat Has {0} Records".format(len(plotCat)))
print("Number of colums = ", len(cols))
| SEP | DATECODE | AGELVLT | LOCT | OCCFAMT | PATCOT | PPGROUPT | TOATYPT | TOAT | WSTYPT | WORKSCHT | |
|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | NS | 201410 | 20-24 | 42-PENNSYLVANIA | 11xx-BUSINESS AND INDUSTRY | Professional | Standard GSEG Pay Plans | Permanent | 10-Competitive Service - Career | Full-time | Full-time Nonseasonal |
| 1 | NS | 201410 | 25-29 | 49-UTAH | 11xx-BUSINESS AND INDUSTRY | Professional | Standard GSEG Pay Plans | Permanent | 10-Competitive Service - Career | Full-time | Full-time Nonseasonal |
| 2 | NS | 201410 | 25-29 | 24-MARYLAND | 11xx-BUSINESS AND INDUSTRY | Professional | Standard GSEG Pay Plans | Permanent | 38-Excepted Service - Other | Full-time | Full-time Nonseasonal |
| 3 | NS | 201410 | 25-29 | 24-MARYLAND | 05xx-ACCOUNTING AND BUDGET | Professional | Standard GSEG Pay Plans | Permanent | 10-Competitive Service - Career | Full-time | Full-time Nonseasonal |
| 4 | NS | 201410 | 25-29 | 24-MARYLAND | 06xx-MEDICAL, HOSPITAL, DENTAL & PUB HEALTH | Professional | Standard GSEG Pay Plans | Permanent | 15-Competitive Service - Career-Conditional | Full-time | Full-time Nonseasonal |
plotCat Has 14920 Records Number of colums = 11 CPU times: user 15.3 ms, sys: 2.8 ms, total: 18.1 ms Wall time: 16.2 ms
%%time
for i in cols:
if i != 'SEP':
plt.figure(i) # Required to create new figure each loop rather than drawing over previous object
f, (ax1, ax2) = plt.subplots(ncols=2, figsize=(20, 10), sharey=False)
sns.countplot(y=i, data=plotCat, color="lightblue", ax=ax1);
sns.countplot(y=i, data=plotCat, hue="SEP", palette="hls", ax=ax2);
if i == 'AGYSUB':
subCountPlot(i, 'SEP', 10000)
elif i == 'LOCT':
subCountPlot(i, 'SEP', 1000)
elif i == 'OCCT':
subCountPlot(i, 'SEP', 2000)
elif i == 'PPGRD':
subCountPlot(i, 'SEP', 6000)
elif i == 'AGYT':
subCountPlot(i, 'SEP', 3000)
CPU times: user 2.1 s, sys: 28.4 ms, total: 2.13 s Wall time: 2.11 s
/usr/local/es7/lib/python3.5/site-packages/matplotlib/pyplot.py:524: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`). max_open_warning, RuntimeWarning)
<matplotlib.figure.Figure at 0x7f6ca3817198>
/usr/local/es7/lib/python3.5/site-packages/matplotlib/font_manager.py:1297: UserWarning: findfont: Font family ['sans-serif'] not found. Falling back to DejaVu Sans (prop.get_family(), self.defaultFamily[fontext]))
<matplotlib.figure.Figure at 0x7f71aabdc470>
<matplotlib.figure.Figure at 0x7f71b60eda20>
<matplotlib.figure.Figure at 0x7f728565e080>
<matplotlib.figure.Figure at 0x7f71b0f84518>
<matplotlib.figure.Figure at 0x7f72858010b8>
<matplotlib.figure.Figure at 0x7f7198c1deb8>
<matplotlib.figure.Figure at 0x7f719a44b828>
<matplotlib.figure.Figure at 0x7f719cc58ba8>
<matplotlib.figure.Figure at 0x7f719e47f1d0>
%%time
for i in cols:
if i != 'SEP':
percBarPlot(i, 'SEP', len(plotCat.SEP.drop_duplicates()))
CPU times: user 1.08 s, sys: 45.4 ms, total: 1.12 s Wall time: 1.06 s
/usr/local/es7/lib/python3.5/site-packages/matplotlib/font_manager.py:1297: UserWarning: findfont: Font family ['sans-serif'] not found. Falling back to DejaVu Sans (prop.get_family(), self.defaultFamily[fontext]))
%%time
sns.set(style="whitegrid", palette="pastel", color_codes=True)
sns.violinplot(x="PATCOT", y="SALARY", data=SampledOPMDataProf, split=True,
inner="quart")
sns.despine(left=True)
CPU times: user 1.66 s, sys: 8.67 s, total: 10.3 s Wall time: 342 ms
/usr/local/es7/lib/python3.5/site-packages/matplotlib/font_manager.py:1297: UserWarning: findfont: Font family ['sans-serif'] not found. Falling back to DejaVu Sans (prop.get_family(), self.defaultFamily[fontext]))
%%time
# Draw a nested violinplot and split the violins for easier comparison
sns.violinplot(x="SEP", y="SALARY", data=SampledOPMDataProf, split=True,
inner="box", scale="area", cut=0)
sns.despine(left=True)
CPU times: user 160 ms, sys: 111 ms, total: 271 ms Wall time: 135 ms
/usr/local/es7/lib/python3.5/site-packages/matplotlib/font_manager.py:1297: UserWarning: findfont: Font family ['sans-serif'] not found. Falling back to DejaVu Sans (prop.get_family(), self.defaultFamily[fontext]))
#%%time
#
#sns.factorplot(x="SEP", y="SALARY", col="PATCOT",
# data=SampledOPMDataProf,
# kind="violin", split=True, aspect=.5, size=15);
#%%time
#
#sns.factorplot(x="SEP", y="SALARY", col="PATCOT", data=SampledOPMDataProf,
# kind="violin", split=True, aspect=.4, size=10);
%%time
g = sns.PairGrid(data=SampledOPMDataProf,
x_vars=["SEP","PATCOT"],
y_vars=["SALARY", "LOS", "LowerLimitAge", "YearsToRetirement"],
aspect=1, size=10)
g.map(sns.violinplot, palette="pastel", inner="quart");
/usr/local/es7/lib/python3.5/site-packages/matplotlib/font_manager.py:1297: UserWarning: findfont: Font family ['sans-serif'] not found. Falling back to DejaVu Sans (prop.get_family(), self.defaultFamily[fontext]))
CPU times: user 4.68 s, sys: 23.7 s, total: 28.4 s Wall time: 1.35 s
Now that we have the dataset sampled, we still have some legwork necessary to convert our categorical attributes into binary integer values. Below we walk through this process for the following Attributes:
Once these attributes have been encoded and description columns removed, we end up with a total of 2446 attributes in our dataset for analysis in our model generation.
# Clean up old objects no longer needed, to clear up memory
process = psutil.Process(os.getpid())
print("Memory Usage before Cleanup: ", process.memory_info().rss)
if 'AGELVL' in dir():
del AGELVL
if 'AggIndAvgSalary' in dir():
del AggIndAvgSalary
if 'AggIndAvgSalary2' in dir():
del AggIndAvgSalary2
if 'AggSEPCount_EFDATE_LOC' in dir():
del AggSEPCount_EFDATE_LOC
if 'AggSEPCount_EFDATE_OCC' in dir():
del AggSEPCount_EFDATE_OCC
if 'AggStrat' in dir():
del AggStrat
if 'DATECODE' in dir():
del DATECODE
if 'EMPColList' in dir():
del EMPColList
if 'EMPDataOrig4Q' in dir():
del EMPDataOrig4Q
if 'maxSize' in dir():
del maxSize
if 'OPMColList' in dir():
del OPMColList
if 'OPMDataFiles' in dir():
del OPMDataFiles
if 'OPMDataList' in dir():
del OPMDataList
if 'OPMDataMerged' in dir():
del OPMDataMerged
if 'OPMDataOrig' in dir():
del OPMDataOrig
if 'SEP' in dir():
del SEP
if 'SampleSize' in dir():
del SampleSize
if 'SampledOPMStratumData' in dir():
del SampledOPMStratumData
if 'SampledOPMStratumDataList' in dir():
del SampledOPMStratumDataList
if 'StratCountSample' in dir():
del StratCountSample
if 'StratSampleSize' in dir():
del StratSampleSize
if 'JTL' in dir():
del JTL
process = psutil.Process(os.getpid())
print("Memory Usage after Cleanup: ", process.memory_info().rss)
Memory Usage before Cleanup: 14041321472 Memory Usage after Cleanup: 12331384832
display(SampledOPMDataProf.head())
SampledOPMDataProf.info()
| SEP | DATECODE | AGELVL | GSEGRD | LOC | PATCO | TOA | WORKSCH | SALARY | LOS | AGELVLT | LOCTYP | LOCTYPT | LOCT | OCCTYP | OCCTYPT | OCCFAM | OCCFAMT | PATCOT | PPTYP | PPTYPT | PPGROUP | PPGROUPT | TOATYP | TOATYPT | TOAT | WSTYP | WSTYPT | WORKSCHT | SEPCount_EFDATE_OCC | SEPCount_EFDATE_LOC | IndAvgSalary | SalaryOverUnderIndAvg | LowerLimitAge | YearsToRetirement | BLS_FEDERAL_OtherSep_Rate | BLS_FEDERAL_Quits_Rate | BLS_FEDERAL_TotalSep_Level | BLS_FEDERAL_JobOpenings_Rate | BLS_FEDERAL_OtherSep_Level | BLS_FEDERAL_Quits_Level | BLS_FEDERAL_JobOpenings_Level | BLS_FEDERAL_Layoffs_Rate | BLS_FEDERAL_Layoffs_Level | BLS_FEDERAL_TotalSep_Rate | SALARYLog | LOSSqrt | SEPCount_EFDATE_OCCLog | SEPCount_EFDATE_LOCLog | IndAvgSalaryLog | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | NS | 201410 | B | 11.0 | 42 | 1 | 10 | F | 61857.0 | 4.7 | 20-24 | 1 | United States | 42-PENNSYLVANIA | 1 | White Collar | 11 | 11xx-BUSINESS AND INDUSTRY | Professional | 1 | General Schedule and Equivalently Graded (GSEG... | 11 | Standard GSEG Pay Plans | 1 | Permanent | 10-Competitive Service - Career | 1 | Full-time | Full-time Nonseasonal | 336.0 | 470 | 65898.205859 | -4041.205859 | 20.0 | 37.0 | 0.4 | 0.4 | 34 | 2.1 | 10 | 11 | 58 | 0.5 | 13 | 1.2 | 11.032581 | 2.167948 | 5.817111 | 6.152733 | 11.095866 |
| 1 | NS | 201410 | C | 12.0 | 49 | 1 | 10 | F | 71813.0 | 7.2 | 25-29 | 1 | United States | 49-UTAH | 1 | White Collar | 11 | 11xx-BUSINESS AND INDUSTRY | Professional | 1 | General Schedule and Equivalently Graded (GSEG... | 11 | Standard GSEG Pay Plans | 1 | Permanent | 10-Competitive Service - Career | 1 | Full-time | Full-time Nonseasonal | 336.0 | 513 | 81218.917413 | -9405.917413 | 25.0 | 32.0 | 0.4 | 0.4 | 34 | 2.1 | 10 | 11 | 58 | 0.5 | 13 | 1.2 | 11.181821 | 2.683282 | 5.817111 | 6.240276 | 11.304903 |
| 2 | NS | 201410 | C | 11.0 | 24 | 1 | 38 | F | 63091.0 | 4.0 | 25-29 | 1 | United States | 24-MARYLAND | 1 | White Collar | 11 | 11xx-BUSINESS AND INDUSTRY | Professional | 1 | General Schedule and Equivalently Graded (GSEG... | 11 | Standard GSEG Pay Plans | 1 | Permanent | 38-Excepted Service - Other | 1 | Full-time | Full-time Nonseasonal | 336.0 | 923 | 65898.205859 | -2807.205859 | 25.0 | 32.0 | 0.4 | 0.4 | 34 | 2.1 | 10 | 11 | 58 | 0.5 | 13 | 1.2 | 11.052333 | 2.000000 | 5.817111 | 6.827629 | 11.095866 |
| 3 | NS | 201410 | C | 12.0 | 24 | 1 | 10 | F | 75621.0 | 5.8 | 25-29 | 1 | United States | 24-MARYLAND | 1 | White Collar | 05 | 05xx-ACCOUNTING AND BUDGET | Professional | 1 | General Schedule and Equivalently Graded (GSEG... | 11 | Standard GSEG Pay Plans | 1 | Permanent | 10-Competitive Service - Career | 1 | Full-time | Full-time Nonseasonal | 75.0 | 923 | 82168.243394 | -6547.243394 | 25.0 | 32.0 | 0.4 | 0.4 | 34 | 2.1 | 10 | 11 | 58 | 0.5 | 13 | 1.2 | 11.233489 | 2.408319 | 4.317488 | 6.827629 | 11.316524 |
| 4 | NS | 201410 | C | 13.0 | 24 | 1 | 15 | F | 95919.0 | 2.0 | 25-29 | 1 | United States | 24-MARYLAND | 1 | White Collar | 06 | 06xx-MEDICAL, HOSPITAL, DENTAL & PUB HEALTH | Professional | 1 | General Schedule and Equivalently Graded (GSEG... | 11 | Standard GSEG Pay Plans | 1 | Permanent | 15-Competitive Service - Career-Conditional | 1 | Full-time | Full-time Nonseasonal | 63.0 | 923 | 121938.733696 | -26019.733696 | 25.0 | 32.0 | 0.4 | 0.4 | 34 | 2.1 | 10 | 11 | 58 | 0.5 | 13 | 1.2 | 11.471259 | 1.414214 | 4.143135 | 6.827629 | 11.711274 |
<class 'pandas.core.frame.DataFrame'> RangeIndex: 14920 entries, 0 to 14919 Data columns (total 50 columns): SEP 14920 non-null object DATECODE 14920 non-null object AGELVL 14920 non-null object GSEGRD 14920 non-null float64 LOC 14920 non-null object PATCO 14920 non-null object TOA 14920 non-null object WORKSCH 14920 non-null object SALARY 14920 non-null float64 LOS 14920 non-null float64 AGELVLT 14920 non-null object LOCTYP 14920 non-null object LOCTYPT 14920 non-null object LOCT 14920 non-null object OCCTYP 14920 non-null object OCCTYPT 14920 non-null object OCCFAM 14920 non-null object OCCFAMT 14920 non-null object PATCOT 14920 non-null object PPTYP 14920 non-null object PPTYPT 14920 non-null object PPGROUP 14920 non-null object PPGROUPT 14920 non-null object TOATYP 14920 non-null object TOATYPT 14920 non-null object TOAT 14920 non-null object WSTYP 14920 non-null object WSTYPT 14920 non-null object WORKSCHT 14920 non-null object SEPCount_EFDATE_OCC 14920 non-null float64 SEPCount_EFDATE_LOC 14920 non-null int64 IndAvgSalary 14920 non-null float64 SalaryOverUnderIndAvg 14920 non-null float64 LowerLimitAge 14920 non-null float64 YearsToRetirement 14920 non-null float64 BLS_FEDERAL_OtherSep_Rate 14920 non-null float64 BLS_FEDERAL_Quits_Rate 14920 non-null float64 BLS_FEDERAL_TotalSep_Level 14920 non-null int64 BLS_FEDERAL_JobOpenings_Rate 14920 non-null float64 BLS_FEDERAL_OtherSep_Level 14920 non-null int64 BLS_FEDERAL_Quits_Level 14920 non-null int64 BLS_FEDERAL_JobOpenings_Level 14920 non-null int64 BLS_FEDERAL_Layoffs_Rate 14920 non-null float64 BLS_FEDERAL_Layoffs_Level 14920 non-null int64 BLS_FEDERAL_TotalSep_Rate 14920 non-null float64 SALARYLog 14920 non-null float64 LOSSqrt 14920 non-null float64 SEPCount_EFDATE_OCCLog 14920 non-null float64 SEPCount_EFDATE_LOCLog 14920 non-null float64 IndAvgSalaryLog 14920 non-null float64 dtypes: float64(18), int64(6), object(26) memory usage: 5.7+ MB
%%time
if os.path.isfile(PickleJarPath+"/OPMAnalysisDataNoFamBinary.pkl"):
print("Found the File! Loading Pickle Now!")
OPMAnalysisDataNoFamBinary = unpickleObject("OPMAnalysisDataNoFamBinary")
else:
OPMAnalysisDataNoFamBinary = SampledOPMDataProf.copy()
cols = ["GENDER",
"DATECODE",
"QTR",
"COUNT",
"AGYTYPT",
"AGYT",
"AGYSUB",
"AGYSUBT",
"QTR",
"AGELVLT",
"LOSLVL",
"LOSLVLT",
"LOCTYPT",
"LOCT",
"OCCTYP",
"OCCTYPT",
"OCCFAM",
"OCCFAMT",
"OCC",
"OCCT",
"PATCO",
"PPGRD",
"PATCOT",
"PPTYPT",
"PPGROUPT",
"PAYPLAN",
"PAYPLANT",
"SALLVLT",
"TOATYPT",
"TOAT",
"WSTYP",
"WSTYPT",
"WORKSCH",
"WORKSCHT",
"SALARY",
"LOS",
"SEPCount_EFDATE_OCC",
"SEPCount_EFDATE_LOC"
]
#delete cols from analysis data
for col in cols:
if col in list(OPMAnalysisDataNoFamBinary.columns):
del OPMAnalysisDataNoFamBinary[col]
OPMAnalysisDataNoFamBinary.info()
cols = ["AGELVL",
"LOC",
"SALLVL",
"TOA",
"AGYTYP",
"AGY",
"LOCTYP",
"PPTYP",
"PPGROUP",
"TOATYP"
]
#Split Values for cols
for col in cols:
if col in list(OPMAnalysisDataNoFamBinary.columns):
AttSplit = pd.get_dummies(OPMAnalysisDataNoFamBinary[col],prefix=col)
display(AttSplit.head())
OPMAnalysisDataNoFamBinary = pd.concat((OPMAnalysisDataNoFamBinary,AttSplit),axis=1) # add back into the dataframe
del OPMAnalysisDataNoFamBinary[col]
pickleObject(OPMAnalysisDataNoFamBinary, "OPMAnalysisDataNoFamBinary")
display(OPMAnalysisDataNoFamBinary.head())
print("Number of Columns: ",len(OPMAnalysisDataNoFamBinary.columns))
OPMAnalysisDataNoFamBinary.info()
Found the File! Loading Pickle Now!
| SEP | GSEGRD | IndAvgSalary | SalaryOverUnderIndAvg | LowerLimitAge | YearsToRetirement | BLS_FEDERAL_OtherSep_Rate | BLS_FEDERAL_Quits_Rate | BLS_FEDERAL_TotalSep_Level | BLS_FEDERAL_JobOpenings_Rate | BLS_FEDERAL_OtherSep_Level | BLS_FEDERAL_Quits_Level | BLS_FEDERAL_JobOpenings_Level | BLS_FEDERAL_Layoffs_Rate | BLS_FEDERAL_Layoffs_Level | BLS_FEDERAL_TotalSep_Rate | SALARYLog | LOSSqrt | SEPCount_EFDATE_OCCLog | SEPCount_EFDATE_LOCLog | IndAvgSalaryLog | AGELVL_B | AGELVL_C | AGELVL_D | AGELVL_E | AGELVL_F | AGELVL_G | AGELVL_H | AGELVL_I | AGELVL_J | AGELVL_K | LOC_01 | LOC_02 | LOC_04 | LOC_05 | LOC_06 | LOC_08 | LOC_09 | LOC_10 | LOC_11 | LOC_12 | LOC_13 | LOC_15 | LOC_16 | LOC_17 | LOC_18 | LOC_19 | LOC_20 | LOC_21 | LOC_22 | LOC_23 | LOC_24 | LOC_25 | LOC_26 | LOC_27 | LOC_28 | LOC_29 | LOC_30 | LOC_31 | LOC_32 | LOC_33 | LOC_34 | LOC_35 | LOC_36 | LOC_37 | LOC_38 | LOC_39 | LOC_40 | LOC_41 | LOC_42 | LOC_44 | LOC_45 | LOC_46 | LOC_47 | LOC_48 | LOC_49 | LOC_50 | LOC_51 | LOC_53 | LOC_54 | LOC_55 | LOC_56 | TOA_10 | TOA_15 | TOA_20 | TOA_30 | TOA_32 | TOA_35 | TOA_38 | TOA_40 | TOA_42 | TOA_44 | TOA_45 | TOA_48 | LOCTYP_1 | PPTYP_1 | PPGROUP_11 | PPGROUP_12 | TOATYP_1 | TOATYP_2 | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | NS | 11.0 | 65898.205859 | -4041.205859 | 20.0 | 37.0 | 0.4 | 0.4 | 34 | 2.1 | 10 | 11 | 58 | 0.5 | 13 | 1.2 | 11.032581 | 2.167948 | 5.817111 | 6.152733 | 11.095866 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 1 | 1 | 0 | 1 | 0 |
| 1 | NS | 12.0 | 81218.917413 | -9405.917413 | 25.0 | 32.0 | 0.4 | 0.4 | 34 | 2.1 | 10 | 11 | 58 | 0.5 | 13 | 1.2 | 11.181821 | 2.683282 | 5.817111 | 6.240276 | 11.304903 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 1 | 1 | 0 | 1 | 0 |
| 2 | NS | 11.0 | 65898.205859 | -2807.205859 | 25.0 | 32.0 | 0.4 | 0.4 | 34 | 2.1 | 10 | 11 | 58 | 0.5 | 13 | 1.2 | 11.052333 | 2.000000 | 5.817111 | 6.827629 | 11.095866 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 1 | 1 | 1 | 0 | 1 | 0 |
| 3 | NS | 12.0 | 82168.243394 | -6547.243394 | 25.0 | 32.0 | 0.4 | 0.4 | 34 | 2.1 | 10 | 11 | 58 | 0.5 | 13 | 1.2 | 11.233489 | 2.408319 | 4.317488 | 6.827629 | 11.316524 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 1 | 1 | 0 | 1 | 0 |
| 4 | NS | 13.0 | 121938.733696 | -26019.733696 | 25.0 | 32.0 | 0.4 | 0.4 | 34 | 2.1 | 10 | 11 | 58 | 0.5 | 13 | 1.2 | 11.471259 | 1.414214 | 4.143135 | 6.827629 | 11.711274 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 1 | 1 | 0 | 1 | 0 |
Number of Columns: 100 <class 'pandas.core.frame.DataFrame'> RangeIndex: 14920 entries, 0 to 14919 Data columns (total 100 columns): SEP 14920 non-null object GSEGRD 14920 non-null float64 IndAvgSalary 14920 non-null float64 SalaryOverUnderIndAvg 14920 non-null float64 LowerLimitAge 14920 non-null float64 YearsToRetirement 14920 non-null float64 BLS_FEDERAL_OtherSep_Rate 14920 non-null float64 BLS_FEDERAL_Quits_Rate 14920 non-null float64 BLS_FEDERAL_TotalSep_Level 14920 non-null int64 BLS_FEDERAL_JobOpenings_Rate 14920 non-null float64 BLS_FEDERAL_OtherSep_Level 14920 non-null int64 BLS_FEDERAL_Quits_Level 14920 non-null int64 BLS_FEDERAL_JobOpenings_Level 14920 non-null int64 BLS_FEDERAL_Layoffs_Rate 14920 non-null float64 BLS_FEDERAL_Layoffs_Level 14920 non-null int64 BLS_FEDERAL_TotalSep_Rate 14920 non-null float64 SALARYLog 14920 non-null float64 LOSSqrt 14920 non-null float64 SEPCount_EFDATE_OCCLog 14920 non-null float64 SEPCount_EFDATE_LOCLog 14920 non-null float64 IndAvgSalaryLog 14920 non-null float64 AGELVL_B 14920 non-null uint8 AGELVL_C 14920 non-null uint8 AGELVL_D 14920 non-null uint8 AGELVL_E 14920 non-null uint8 AGELVL_F 14920 non-null uint8 AGELVL_G 14920 non-null uint8 AGELVL_H 14920 non-null uint8 AGELVL_I 14920 non-null uint8 AGELVL_J 14920 non-null uint8 AGELVL_K 14920 non-null uint8 LOC_01 14920 non-null uint8 LOC_02 14920 non-null uint8 LOC_04 14920 non-null uint8 LOC_05 14920 non-null uint8 LOC_06 14920 non-null uint8 LOC_08 14920 non-null uint8 LOC_09 14920 non-null uint8 LOC_10 14920 non-null uint8 LOC_11 14920 non-null uint8 LOC_12 14920 non-null uint8 LOC_13 14920 non-null uint8 LOC_15 14920 non-null uint8 LOC_16 14920 non-null uint8 LOC_17 14920 non-null uint8 LOC_18 14920 non-null uint8 LOC_19 14920 non-null uint8 LOC_20 14920 non-null uint8 LOC_21 14920 non-null uint8 LOC_22 14920 non-null uint8 LOC_23 14920 non-null uint8 LOC_24 14920 non-null uint8 LOC_25 14920 non-null uint8 LOC_26 14920 non-null uint8 LOC_27 14920 non-null uint8 LOC_28 14920 non-null uint8 LOC_29 14920 non-null uint8 LOC_30 14920 non-null uint8 LOC_31 14920 non-null uint8 LOC_32 14920 non-null uint8 LOC_33 14920 non-null uint8 LOC_34 14920 non-null uint8 LOC_35 14920 non-null uint8 LOC_36 14920 non-null uint8 LOC_37 14920 non-null uint8 LOC_38 14920 non-null uint8 LOC_39 14920 non-null uint8 LOC_40 14920 non-null uint8 LOC_41 14920 non-null uint8 LOC_42 14920 non-null uint8 LOC_44 14920 non-null uint8 LOC_45 14920 non-null uint8 LOC_46 14920 non-null uint8 LOC_47 14920 non-null uint8 LOC_48 14920 non-null uint8 LOC_49 14920 non-null uint8 LOC_50 14920 non-null uint8 LOC_51 14920 non-null uint8 LOC_53 14920 non-null uint8 LOC_54 14920 non-null uint8 LOC_55 14920 non-null uint8 LOC_56 14920 non-null uint8 TOA_10 14920 non-null uint8 TOA_15 14920 non-null uint8 TOA_20 14920 non-null uint8 TOA_30 14920 non-null uint8 TOA_32 14920 non-null uint8 TOA_35 14920 non-null uint8 TOA_38 14920 non-null uint8 TOA_40 14920 non-null uint8 TOA_42 14920 non-null uint8 TOA_44 14920 non-null uint8 TOA_45 14920 non-null uint8 TOA_48 14920 non-null uint8 LOCTYP_1 14920 non-null uint8 PPTYP_1 14920 non-null uint8 PPGROUP_11 14920 non-null uint8 PPGROUP_12 14920 non-null uint8 TOATYP_1 14920 non-null uint8 TOATYP_2 14920 non-null uint8 dtypes: float64(15), int64(5), object(1), uint8(79) memory usage: 3.5+ MB CPU times: user 92.1 ms, sys: 2.09 ms, total: 94.2 ms Wall time: 91.9 ms
Below is a display of all remaining attributes and their corresponding data types for analysis
%%time
data_type = []
for idx, col in enumerate(OPMAnalysisDataNoFamBinary.columns):
data_type.append(OPMAnalysisDataNoFamBinary.dtypes[idx])
summary_df = {'Attribute Name' : pd.Series(OPMAnalysisDataNoFamBinary.columns, index = range(len(OPMAnalysisDataNoFamBinary.columns))), 'Data Type' : pd.Series(data_type, index = range(len(OPMAnalysisDataNoFamBinary.columns)))}
summary_df = pd.DataFrame(summary_df)
display(summary_df)
del data_type, summary_df
| Attribute Name | Data Type | |
|---|---|---|
| 0 | SEP | object |
| 1 | GSEGRD | float64 |
| 2 | IndAvgSalary | float64 |
| 3 | SalaryOverUnderIndAvg | float64 |
| 4 | LowerLimitAge | float64 |
| 5 | YearsToRetirement | float64 |
| 6 | BLS_FEDERAL_OtherSep_Rate | float64 |
| 7 | BLS_FEDERAL_Quits_Rate | float64 |
| 8 | BLS_FEDERAL_TotalSep_Level | int64 |
| 9 | BLS_FEDERAL_JobOpenings_Rate | float64 |
| 10 | BLS_FEDERAL_OtherSep_Level | int64 |
| 11 | BLS_FEDERAL_Quits_Level | int64 |
| 12 | BLS_FEDERAL_JobOpenings_Level | int64 |
| 13 | BLS_FEDERAL_Layoffs_Rate | float64 |
| 14 | BLS_FEDERAL_Layoffs_Level | int64 |
| 15 | BLS_FEDERAL_TotalSep_Rate | float64 |
| 16 | SALARYLog | float64 |
| 17 | LOSSqrt | float64 |
| 18 | SEPCount_EFDATE_OCCLog | float64 |
| 19 | SEPCount_EFDATE_LOCLog | float64 |
| 20 | IndAvgSalaryLog | float64 |
| 21 | AGELVL_B | uint8 |
| 22 | AGELVL_C | uint8 |
| 23 | AGELVL_D | uint8 |
| 24 | AGELVL_E | uint8 |
| 25 | AGELVL_F | uint8 |
| 26 | AGELVL_G | uint8 |
| 27 | AGELVL_H | uint8 |
| 28 | AGELVL_I | uint8 |
| 29 | AGELVL_J | uint8 |
| 30 | AGELVL_K | uint8 |
| 31 | LOC_01 | uint8 |
| 32 | LOC_02 | uint8 |
| 33 | LOC_04 | uint8 |
| 34 | LOC_05 | uint8 |
| 35 | LOC_06 | uint8 |
| 36 | LOC_08 | uint8 |
| 37 | LOC_09 | uint8 |
| 38 | LOC_10 | uint8 |
| 39 | LOC_11 | uint8 |
| 40 | LOC_12 | uint8 |
| 41 | LOC_13 | uint8 |
| 42 | LOC_15 | uint8 |
| 43 | LOC_16 | uint8 |
| 44 | LOC_17 | uint8 |
| 45 | LOC_18 | uint8 |
| 46 | LOC_19 | uint8 |
| 47 | LOC_20 | uint8 |
| 48 | LOC_21 | uint8 |
| 49 | LOC_22 | uint8 |
| 50 | LOC_23 | uint8 |
| 51 | LOC_24 | uint8 |
| 52 | LOC_25 | uint8 |
| 53 | LOC_26 | uint8 |
| 54 | LOC_27 | uint8 |
| 55 | LOC_28 | uint8 |
| 56 | LOC_29 | uint8 |
| 57 | LOC_30 | uint8 |
| 58 | LOC_31 | uint8 |
| 59 | LOC_32 | uint8 |
| 60 | LOC_33 | uint8 |
| 61 | LOC_34 | uint8 |
| 62 | LOC_35 | uint8 |
| 63 | LOC_36 | uint8 |
| 64 | LOC_37 | uint8 |
| 65 | LOC_38 | uint8 |
| 66 | LOC_39 | uint8 |
| 67 | LOC_40 | uint8 |
| 68 | LOC_41 | uint8 |
| 69 | LOC_42 | uint8 |
| 70 | LOC_44 | uint8 |
| 71 | LOC_45 | uint8 |
| 72 | LOC_46 | uint8 |
| 73 | LOC_47 | uint8 |
| 74 | LOC_48 | uint8 |
| 75 | LOC_49 | uint8 |
| 76 | LOC_50 | uint8 |
| 77 | LOC_51 | uint8 |
| 78 | LOC_53 | uint8 |
| 79 | LOC_54 | uint8 |
| 80 | LOC_55 | uint8 |
| 81 | LOC_56 | uint8 |
| 82 | TOA_10 | uint8 |
| 83 | TOA_15 | uint8 |
| 84 | TOA_20 | uint8 |
| 85 | TOA_30 | uint8 |
| 86 | TOA_32 | uint8 |
| 87 | TOA_35 | uint8 |
| 88 | TOA_38 | uint8 |
| 89 | TOA_40 | uint8 |
| 90 | TOA_42 | uint8 |
| 91 | TOA_44 | uint8 |
| 92 | TOA_45 | uint8 |
| 93 | TOA_48 | uint8 |
| 94 | LOCTYP_1 | uint8 |
| 95 | PPTYP_1 | uint8 |
| 96 | PPGROUP_11 | uint8 |
| 97 | PPGROUP_12 | uint8 |
| 98 | TOATYP_1 | uint8 |
| 99 | TOATYP_2 | uint8 |
CPU times: user 24.3 ms, sys: 89 µs, total: 24.4 ms Wall time: 23.7 ms
We also scale the data values to remove bias in our models due to different attribute scales. Without scaling the data, attributes such as SALARY and LOS would carry heavier weights when compared against the binary encoded attributes and BLS data. This would cause unbalanced and improperly analyzed data for model creation.
OPMScaledAnalysisData = OPMAnalysisDataNoFamBinary.copy()
del OPMScaledAnalysisData["SEP"]
%%time
OPMAnalysisScalerFit = MinMaxScaler().fit(OPMScaledAnalysisData)
## Pickle for later re-use if needed
pickleObject(OPMAnalysisScalerFit, "OPMAnalysisScalerFit")
OPMScaledAnalysisData = pd.DataFrame(OPMAnalysisScalerFit.transform(OPMScaledAnalysisData), columns = OPMScaledAnalysisData.columns)
CPU times: user 12.5 ms, sys: 834 µs, total: 13.4 ms Wall time: 14.4 ms
display(OPMScaledAnalysisData.head())
| GSEGRD | IndAvgSalary | SalaryOverUnderIndAvg | LowerLimitAge | YearsToRetirement | BLS_FEDERAL_OtherSep_Rate | BLS_FEDERAL_Quits_Rate | BLS_FEDERAL_TotalSep_Level | BLS_FEDERAL_JobOpenings_Rate | BLS_FEDERAL_OtherSep_Level | BLS_FEDERAL_Quits_Level | BLS_FEDERAL_JobOpenings_Level | BLS_FEDERAL_Layoffs_Rate | BLS_FEDERAL_Layoffs_Level | BLS_FEDERAL_TotalSep_Rate | SALARYLog | LOSSqrt | SEPCount_EFDATE_OCCLog | SEPCount_EFDATE_LOCLog | IndAvgSalaryLog | AGELVL_B | AGELVL_C | AGELVL_D | AGELVL_E | AGELVL_F | AGELVL_G | AGELVL_H | AGELVL_I | AGELVL_J | AGELVL_K | LOC_01 | LOC_02 | LOC_04 | LOC_05 | LOC_06 | LOC_08 | LOC_09 | LOC_10 | LOC_11 | LOC_12 | LOC_13 | LOC_15 | LOC_16 | LOC_17 | LOC_18 | LOC_19 | LOC_20 | LOC_21 | LOC_22 | LOC_23 | LOC_24 | LOC_25 | LOC_26 | LOC_27 | LOC_28 | LOC_29 | LOC_30 | LOC_31 | LOC_32 | LOC_33 | LOC_34 | LOC_35 | LOC_36 | LOC_37 | LOC_38 | LOC_39 | LOC_40 | LOC_41 | LOC_42 | LOC_44 | LOC_45 | LOC_46 | LOC_47 | LOC_48 | LOC_49 | LOC_50 | LOC_51 | LOC_53 | LOC_54 | LOC_55 | LOC_56 | TOA_10 | TOA_15 | TOA_20 | TOA_30 | TOA_32 | TOA_35 | TOA_38 | TOA_40 | TOA_42 | TOA_44 | TOA_45 | TOA_48 | LOCTYP_1 | PPTYP_1 | PPGROUP_11 | PPGROUP_12 | TOATYP_1 | TOATYP_2 | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | 0.500 | 0.145270 | 0.471501 | 0.000000 | 1.000000 | 0.333333 | 0.333333 | 0.235294 | 0.153846 | 0.222222 | 0.25 | 0.083333 | 0.25 | 0.26087 | 0.166667 | 0.220405 | 0.256387 | 0.886424 | 0.646808 | 0.296670 | 1.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 1.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 1.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 1.0 | 0.0 | 1.0 | 0.0 |
| 1 | 0.625 | 0.229804 | 0.449531 | 0.111111 | 0.888889 | 0.333333 | 0.333333 | 0.235294 | 0.153846 | 0.222222 | 0.25 | 0.083333 | 0.25 | 0.26087 | 0.166667 | 0.292431 | 0.317332 | 0.886424 | 0.664165 | 0.418258 | 0.0 | 1.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 1.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 1.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 1.0 | 0.0 | 1.0 | 0.0 |
| 2 | 0.500 | 0.145270 | 0.476554 | 0.111111 | 0.888889 | 0.333333 | 0.333333 | 0.235294 | 0.153846 | 0.222222 | 0.25 | 0.083333 | 0.25 | 0.26087 | 0.166667 | 0.229938 | 0.236525 | 0.886424 | 0.780616 | 0.296670 | 0.0 | 1.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 1.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 1.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 1.0 | 0.0 | 1.0 | 0.0 |
| 3 | 0.625 | 0.235042 | 0.461238 | 0.111111 | 0.888889 | 0.333333 | 0.333333 | 0.235294 | 0.153846 | 0.222222 | 0.25 | 0.083333 | 0.25 | 0.26087 | 0.166667 | 0.317367 | 0.284814 | 0.657909 | 0.780616 | 0.425018 | 0.0 | 1.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 1.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 1.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 1.0 | 0.0 | 1.0 | 0.0 |
| 4 | 0.750 | 0.454481 | 0.381495 | 0.111111 | 0.888889 | 0.333333 | 0.333333 | 0.235294 | 0.153846 | 0.222222 | 0.25 | 0.083333 | 0.25 | 0.26087 | 0.166667 | 0.432120 | 0.167248 | 0.631340 | 0.780616 | 0.654628 | 0.0 | 1.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 1.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 1.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 1.0 | 0.0 | 1.0 | 0.0 |
Our objective, is to reduce dimensionality through identification of principal components. We have chosen to use the full column input (99) as the maximum number of components to be produced. Given our hopes are to reduce the number of attributes needed for a model, we expect to find much smaller than 99 as our Principal components which explain over 80% variance within the dataset. We will review each component's explained variance further to determine the proper number of components to be included later during model generation. Note randomized PCA was chosen in order to use singular value decomposition in our dimensionality reduction efforts due to the large size of our data set.
%%time
seed = len(OPMScaledAnalysisData)
print(OPMScaledAnalysisData.shape)
pca_class = PCA(n_components=len(OPMScaledAnalysisData.columns), svd_solver='randomized', random_state=seed)
pca_class.fit(OPMScaledAnalysisData)
(14920, 99) CPU times: user 11.4 s, sys: 16.6 s, total: 28 s Wall time: 635 ms
Below, the resulting components have been ordered by eigenvector value and these values portrayed as ratios of variance explained by each component. In order to identify the principal components to be included during model generation, we review the rate at which explained variance decreases in significance from one principal component to the next. Accompanying these proportion values is a scree plot representing these same values in visual form. By plotting the scree plot, it is easier to judge where this rate of decreasing explained variance occurs. Note the rate of change in explained variance among the first 8 principal components, with another less significant change through the 22th component. After the 22th component, the rate of decreasing explained variance begins to somewhat flatten out.
%%time
#The amount of variance that each PC explains
var= pca_class.explained_variance_ratio_
sns.set(font_scale=1.7)
plt.plot(range(1,len(OPMScaledAnalysisData.columns)+1), var*100, marker = '.', color = 'red', markerfacecolor = 'black')
plt.xlabel('Principal Components')
plt.ylabel('Percentage of Explained Variance')
plt.title('Scree Plot')
plt.axis([0, len(OPMScaledAnalysisData.columns)+1, -0.1, 9])
plt.annotate('22nd Component', xy=(22, 1.2), xytext=(40, 4),
arrowprops=dict(facecolor='black', shrink=0.05),)
np.set_printoptions(suppress=True)
print(np.round(var, decimals=4)*100)
[ 11.45 9.85 6.05 5.42 4.49 3.84 3.61 3.41 3.13 2.99 2.76 2.74 2.72 2.6 2.53 2.36 2.31 2.05 1.89 1.68 1.64 1.18 1.09 0.96 0.93 0.91 0.81 0.78 0.68 0.66 0.64 0.61 0.58 0.54 0.51 0.49 0.46 0.45 0.44 0.41 0.39 0.39 0.36 0.36 0.3 0.3 0.29 0.28 0.28 0.28 0.26 0.22 0.21 0.21 0.21 0.21 0.2 0.2 0.2 0.19 0.18 0.17 0.17 0.16 0.13 0.13 0.11 0.11 0.1 0.1 0.1 0.1 0.07 0.07 0.06 0.06 0.04 0.04 0.03 0.02 0.02 0.02 0.01 0.01 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. ] CPU times: user 359 ms, sys: 2.27 s, total: 2.63 s Wall time: 68 ms
/usr/local/es7/lib/python3.5/site-packages/matplotlib/font_manager.py:1297: UserWarning: findfont: Font family ['sans-serif'] not found. Falling back to DejaVu Sans (prop.get_family(), self.defaultFamily[fontext]))
By now referring to the cumulative variance values and associated plot below, it may be seen that the cumulative variance increases in a fairly consistent parabola curve. In attempts to acheive a cumulative variance explained of greater than 80%, we end at 22 principal components. For this reason, 22 principal components may be selected as being the most appropriate for separation classification modeling given the variables among these data.
#Cumulative Variance explains
var1=np.cumsum(np.round(pca_class.explained_variance_ratio_, decimals=4)*100)
plt.plot(range(1,len(OPMScaledAnalysisData.columns)+1), var1, marker = '.', color = 'green', markerfacecolor = 'black')
plt.xlabel('Principal Components')
plt.ylabel('Explained Variance (Sum %)')
plt.title('Cumulative Variance Plot')
plt.axis([0, len(OPMScaledAnalysisData.columns)+1, 10, len(OPMScaledAnalysisData.columns)+1])
plt.annotate('22nd Component', xy=(22, 80.54), xytext=(40, 60),
arrowprops=dict(facecolor='black', shrink=0.05),)
print(var1)
[ 11.45 21.3 27.35 32.77 37.26 41.1 44.71 48.12 51.25 54.24 57. 59.74 62.46 65.06 67.59 69.95 72.26 74.31 76.2 77.88 79.52 80.7 81.79 82.75 83.68 84.59 85.4 86.18 86.86 87.52 88.16 88.77 89.35 89.89 90.4 90.89 91.35 91.8 92.24 92.65 93.04 93.43 93.79 94.15 94.45 94.75 95.04 95.32 95.6 95.88 96.14 96.36 96.57 96.78 96.99 97.2 97.4 97.6 97.8 97.99 98.17 98.34 98.51 98.67 98.8 98.93 99.04 99.15 99.25 99.35 99.45 99.55 99.62 99.69 99.75 99.81 99.85 99.89 99.92 99.94 99.96 99.98 99.99 100. 100. 100. 100. 100. 100. 100. 100. 100. 100. 100. 100. 100. 100. 100. 100. ]
/usr/local/es7/lib/python3.5/site-packages/matplotlib/font_manager.py:1297: UserWarning: findfont: Font family ['sans-serif'] not found. Falling back to DejaVu Sans (prop.get_family(), self.defaultFamily[fontext]))
We proceed to analyze the first 4 component Feature Loadings more carefully. See below, plots of the top 10 loadings for each component.
plt.rcParams['figure.figsize'] = (20, 12)
fig = plt.figure()
plt.rcParams.update({'font.size': 16})
plt.rc('xtick', labelsize=15)
plt.rc('ytick', labelsize=15)
for i in range(0,4):
components = pd.Series(pca_class.components_[i], index=OPMScaledAnalysisData.columns)
maxcomponent = pd.Series(pd.DataFrame(abs(components).sort_values(ascending=False).head(10)).index)
matplotlib.rc('xtick', labelsize=12)
ax = fig.add_subplot(2,2,i + 1)
weightsplot = pd.Series(components, index=maxcomponent)
weightsplot.plot(title = "Principal Component "+ str(i+1), kind='bar', color = 'Tomato', ax = ax)
plt.tight_layout()
plt.show()
/usr/local/es7/lib/python3.5/site-packages/matplotlib/font_manager.py:1297: UserWarning: findfont: Font family ['sans-serif'] not found. Falling back to DejaVu Sans (prop.get_family(), self.defaultFamily[fontext]))
MaxPC = 22
PCList = []
for i in range(0,MaxPC):
components = pd.Series(pca_class.components_[i], index=OPMScaledAnalysisData.columns)
maxcomponent = pd.Series(pd.DataFrame(abs(components).sort_values(ascending=False).head(15)).index)
PCList.append(maxcomponent)
PCList = pd.concat(PCList).drop_duplicates().sort_values(ascending=True).reset_index(drop = True)
print(PCList)
PCList = list(PCList)
0 AGELVL_C 1 AGELVL_D 2 AGELVL_E 3 AGELVL_F 4 AGELVL_G 5 AGELVL_H 6 AGELVL_I 7 AGELVL_J 8 AGELVL_K 9 BLS_FEDERAL_JobOpenings_Level 10 BLS_FEDERAL_JobOpenings_Rate 11 BLS_FEDERAL_Layoffs_Level 12 BLS_FEDERAL_Layoffs_Rate 13 BLS_FEDERAL_OtherSep_Level 14 BLS_FEDERAL_OtherSep_Rate 15 BLS_FEDERAL_Quits_Level 16 BLS_FEDERAL_Quits_Rate 17 BLS_FEDERAL_TotalSep_Level 18 BLS_FEDERAL_TotalSep_Rate 19 GSEGRD 20 IndAvgSalary 21 IndAvgSalaryLog 22 LOC_04 23 LOC_06 24 LOC_08 25 LOC_11 26 LOC_12 27 LOC_13 28 LOC_24 29 LOC_36 30 LOC_48 31 LOC_51 32 LOSSqrt 33 LowerLimitAge 34 PPGROUP_11 35 PPGROUP_12 36 SALARYLog 37 SEPCount_EFDATE_LOCLog 38 SEPCount_EFDATE_OCCLog 39 TOATYP_1 40 TOATYP_2 41 TOA_10 42 TOA_15 43 TOA_20 44 TOA_30 45 TOA_38 46 TOA_40 47 TOA_48 48 YearsToRetirement dtype: object
Total of 50 features of the original 99 are identified, by taking the top 15 feature loadings within the first 22 components as determined above as the appropriate components to maximize variance explained. We may now, optionally utilize these 50 features identified, or utilize principal component vectors for analysis in the next steps.
Due to the unproportional number of observations in each separation type in our dataset, we need to create weightings. using SciKit's class_weight algorithm, we compute an array of weights to be used downstream in our models.
We have chosen to utilize Stratified KFold Cross Validation for our classification analysis, with 5 folds. This means, that from our original sample size of 16,638, each "fold" will save off approximately 20% as test observations utilizing the rest as training observations all while keeping the ratio of classes equal amongst customers and subscribers. This process will occur through 5 iterations, or folds, to allow us to cross validate our results amongst different test/train combinations. We have utilized a random_state seed equal to the length of the original sampled dataset to ensure reproducible results.
seed = len(OPMAnalysisDataNoFamBinary)
cv = StratifiedKFold(n_splits = 5, random_state = seed)
print(OPMAnalysisDataNoFamBinary.shape)
print(cv)
(14920, 100) StratifiedKFold(n_splits=5, random_state=14920, shuffle=False)
Max Depth The maximum depth (levels) in the tree. When a value is set, the tree may not split further once this level has been met regardless of how many nodes are in the leaf.
Max Features Number of features to consider when looking for a split.
Minimum Samples in Leaf Minimum number of samples required to be in a leaf node. Splits may not occur which cause the number of samples in a leaf to be less than this value. Too low a value here leads to overfitting the tree to train data.
Minimum Samples to Split Minimum number fo samples required to split a node. Care was taken during parameter tests to keep the ratio between Min Samples in Leaf and Min Samples to Split equal to that of the default values (1:2). This was done to allow an even 50/50 split on nodes which match the lowest granularity split criteria. similar to the min samples in leaf, too low a value here leads to overfitting the tree to train data.
n_estimators Number of Trees generated in the forest. Increasing the number of trees, in our models increased accuracy while decreasing performance. We tuned to provide output that completed all 10 iterations in under 10 minutes.
| max_depth | max_features | min_samples_leaf | min_samples_split | n_estimators |
|---|---|---|---|---|
| TBD | TBD | TBD | TBD | TBD |
%%time
"""
def rfc_explor(n_estimators,
max_features,
max_depth,
min_samples_split,
min_samples_leaf,
Data = OPMAnalysisDataNoFam,
cols = PCList,
cv = cv,
seed = seed):
startTime = datetime.now()
y = Data["SEP"].values # get the labels we want
X = Data[cols].as_matrix()
rfc_clf = RandomForestClassifier(n_estimators=n_estimators, max_features = max_features, max_depth=max_depth, min_samples_split = min_samples_split, min_samples_leaf = min_samples_leaf, class_weight = "balanced", n_jobs=-1, random_state = seed) # get object
# setup pipeline to take PCA, then fit a clf model
clf_pipe = Pipeline(
[('minMaxScaler', MinMaxScaler()),
('CLF',rfc_clf)]
)
accuracy = cross_val_score(clf_pipe, X, y, cv=cv.split(X, y)) # this also can help with parallelism
MeanAccuracy = sum(accuracy)/len(accuracy)
accuracy = np.append(accuracy, MeanAccuracy)
endTime = datetime.now()
TotalTime = endTime - startTime
accuracy = np.append(accuracy, TotalTime)
print(TotalTime)
print(accuracy)
return accuracy
"""
CPU times: user 3 µs, sys: 1e+03 ns, total: 4 µs Wall time: 8.58 µs
'\ndef rfc_explor(n_estimators,\n max_features,\n max_depth, \n min_samples_split,\n min_samples_leaf,\n Data = OPMAnalysisDataNoFam,\n cols = PCList,\n cv = cv,\n seed = seed):\n startTime = datetime.now()\n y = Data["SEP"].values # get the labels we want \n \n X = Data[cols].as_matrix()\n \n rfc_clf = RandomForestClassifier(n_estimators=n_estimators, max_features = max_features, max_depth=max_depth, min_samples_split = min_samples_split, min_samples_leaf = min_samples_leaf, class_weight = "balanced", n_jobs=-1, random_state = seed) # get object\n \n # setup pipeline to take PCA, then fit a clf model\n clf_pipe = Pipeline(\n [(\'minMaxScaler\', MinMaxScaler()),\n (\'CLF\',rfc_clf)]\n )\n\n accuracy = cross_val_score(clf_pipe, X, y, cv=cv.split(X, y)) # this also can help with parallelism\n MeanAccuracy = sum(accuracy)/len(accuracy)\n accuracy = np.append(accuracy, MeanAccuracy)\n endTime = datetime.now()\n TotalTime = endTime - startTime\n accuracy = np.append(accuracy, TotalTime)\n \n print(TotalTime)\n print(accuracy)\n \n return accuracy\n'
%%time
"""
def rfc_explor_w_PCA(n_estimators,
max_features,
max_depth,
min_samples_split,
min_samples_leaf,
PCA,
Data = OPMAnalysisDataNoFam,
cv = cv,
seed = seed):
startTime = datetime.now()
y = Data["SEP"].values # get the labels we want
X = Data.drop("SEP", axis=1).as_matrix()
rfc_clf = RandomForestClassifier(n_estimators=n_estimators, max_features = max_features, max_depth=max_depth, min_samples_split = min_samples_split, min_samples_leaf = min_samples_leaf, class_weight = "balanced", n_jobs=-1, random_state = seed) # get object
# setup pipeline to take PCA, then fit a clf model
clf_pipe = Pipeline(
[('minMaxScaler', MinMaxScaler()),
('PCA', PCA),
('CLF',rfc_clf)]
)
accuracy = cross_val_score(clf_pipe, X, y, cv=cv.split(X, y)) # this also can help with parallelism
MeanAccuracy = sum(accuracy)/len(accuracy)
accuracy = np.append(accuracy, MeanAccuracy)
endTime = datetime.now()
TotalTime = endTime - startTime
accuracy = np.append(accuracy, TotalTime)
#print(TotalTime)
#print(accuracy)
return accuracy
"""
CPU times: user 4 µs, sys: 1e+03 ns, total: 5 µs Wall time: 8.11 µs
'\ndef rfc_explor_w_PCA(n_estimators,\n max_features,\n max_depth, \n min_samples_split,\n min_samples_leaf,\n PCA,\n Data = OPMAnalysisDataNoFam,\n cv = cv,\n seed = seed):\n startTime = datetime.now()\n y = Data["SEP"].values # get the labels we want \n \n X = Data.drop("SEP", axis=1).as_matrix()\n \n rfc_clf = RandomForestClassifier(n_estimators=n_estimators, max_features = max_features, max_depth=max_depth, min_samples_split = min_samples_split, min_samples_leaf = min_samples_leaf, class_weight = "balanced", n_jobs=-1, random_state = seed) # get object\n \n # setup pipeline to take PCA, then fit a clf model\n clf_pipe = Pipeline(\n [(\'minMaxScaler\', MinMaxScaler()),\n (\'PCA\', PCA),\n (\'CLF\',rfc_clf)]\n )\n\n accuracy = cross_val_score(clf_pipe, X, y, cv=cv.split(X, y)) # this also can help with parallelism\n MeanAccuracy = sum(accuracy)/len(accuracy)\n accuracy = np.append(accuracy, MeanAccuracy)\n endTime = datetime.now()\n TotalTime = endTime - startTime\n accuracy = np.append(accuracy, TotalTime)\n \n #print(TotalTime)\n #print(accuracy)\n \n return accuracy\n'
%%time
"""
def rfc_explor_w_PCA(n_estimators,
max_features,
max_depth,
min_samples_split,
min_samples_leaf,
PCA,
Data = OPMAnalysisDataNoFam,
cv = cv,
seed = seed):
startTime = datetime.now()
y = Data["SEP"].values # get the labels we want
X = Data.drop("SEP", axis=1).as_matrix()
rfc_clf = RandomForestClassifier(n_estimators=n_estimators, max_features = max_features, max_depth=max_depth, min_samples_split = min_samples_split, min_samples_leaf = min_samples_leaf, class_weight = "balanced", n_jobs=-1, random_state = seed) # get object
# setup pipeline to take PCA, then fit a clf model
clf_pipe = Pipeline(
[('minMaxScaler', MinMaxScaler()),
('PCA', PCA),
('CLF',rfc_clf)]
)
accuracy = cross_val_score(clf_pipe, X, y, cv=cv.split(X, y)) # this also can help with parallelism
MeanAccuracy = sum(accuracy)/len(accuracy)
accuracy = np.append(accuracy, MeanAccuracy)
endTime = datetime.now()
TotalTime = endTime - startTime
accuracy = np.append(accuracy, TotalTime)
#print(TotalTime)
#print(accuracy)
return accuracy
"""
CPU times: user 3 µs, sys: 1e+03 ns, total: 4 µs Wall time: 8.11 µs
'\ndef rfc_explor_w_PCA(n_estimators,\n max_features,\n max_depth, \n min_samples_split,\n min_samples_leaf,\n PCA,\n Data = OPMAnalysisDataNoFam,\n cv = cv,\n seed = seed):\n startTime = datetime.now()\n y = Data["SEP"].values # get the labels we want \n \n X = Data.drop("SEP", axis=1).as_matrix()\n \n rfc_clf = RandomForestClassifier(n_estimators=n_estimators, max_features = max_features, max_depth=max_depth, min_samples_split = min_samples_split, min_samples_leaf = min_samples_leaf, class_weight = "balanced", n_jobs=-1, random_state = seed) # get object\n \n # setup pipeline to take PCA, then fit a clf model\n clf_pipe = Pipeline(\n [(\'minMaxScaler\', MinMaxScaler()),\n (\'PCA\', PCA),\n (\'CLF\',rfc_clf)]\n )\n\n accuracy = cross_val_score(clf_pipe, X, y, cv=cv.split(X, y)) # this also can help with parallelism\n MeanAccuracy = sum(accuracy)/len(accuracy)\n accuracy = np.append(accuracy, MeanAccuracy)\n endTime = datetime.now()\n TotalTime = endTime - startTime\n accuracy = np.append(accuracy, TotalTime)\n \n #print(TotalTime)\n #print(accuracy)\n \n return accuracy\n'
We have created a function to be re-used for our cross-validation Accuracy Scores. Inputs of PCA components, Model CLF object, original sample data, and a CV containing our test/train splits allow us to easily produce an array of Accuracy Scores for the different permutations of models tested. A XXXXXXTBDXXXXX plot is also displayed depicting a view of the misclassification values for each iteration. Finally, a confusion matrix is displayed for the last test/train iteration for further interpretation on results.
%%time
"""
acclist = []
fullColumns = list(OPMAnalysisDataNoFam.columns)
for i in fullColumns:
if i == "SEP": fullColumns.remove(i)
n_estimators = [10 , 10 , 10 , 10 , 10 , 10 , 10 , 10 , 10 , 10 , 10 , 5 , 15 ]
max_features = ['auto', 'auto' , 'auto', 'auto', 'auto', 'auto', 'auto', 14 , 14 , 14 , 14 , 14 , 14 ]
max_depth = [None , None , None , None , None , None , None , None , 1000 , 500 , 100 , 1000 , 1000 ]
min_samples_split = [2 , 8 , 12 , 16 , 20 , 50 , 80 , 50 , 50 , 50 , 50 , 50 , 50 ]
min_samples_leaf = [1 , 4 , 6 , 8 , 10 , 25 , 40 , 25 , 25 , 25 , 25 , 25 , 25 ]
##Model with all Raw Scaled Features
for i in range(0,len(n_estimators)):
acclist.append(rfc_explor(n_estimators = n_estimators[i],
max_features = max_features[i],
max_depth = max_depth[i],
min_samples_split = min_samples_split[i],
min_samples_leaf = min_samples_leaf[i],
cols = fullColumns
)
)
rfcdf = pd.DataFrame(pd.concat([pd.DataFrame({ "ModelVersion": "All Raw Features",
"n_estimators": n_estimators,
"max_features": max_features,
"max_depth": max_depth,
"min_samples_split": min_samples_split,
"min_samples_leaf": min_samples_leaf
}),
pd.DataFrame(acclist)], axis = 1).reindex())
rfcdf.columns = ['ModelVersion', 'max_depth', 'max_features', 'min_samples_leaf','min_samples_split', 'n_estimators', 'Iteration 0', 'Iteration 1', 'Iteration 2', 'Iteration 3', 'Iteration 4', 'MeanAccuracy', 'RunTime']
display(rfcdf)
del rfcdf, acclist
acclist = []
n_estimators = [10 , 10 , 10 , 10 , 10 , 10 , 10 , 10 , 10 , 10 , 10 , 5 , 15 ]
max_features = ['auto', 'auto' , 'auto', 'auto', 'auto', 'auto', 'auto', 14 , 14 , 14 , 14 , 14 , 14 ]
max_depth = [None , None , None , None , None , None , None , None , 1000 , 500 , 100 , 1000 , 1000 ]
min_samples_split = [2 , 8 , 12 , 16 , 20 , 50 , 80 , 50 , 50 , 50 , 50 , 50 , 50 ]
min_samples_leaf = [1 , 4 , 6 , 8 , 10 , 25 , 40 , 25 , 25 , 25 , 25 , 25 , 25 ]
## Model with only top 15 raw Scaled Principal Features
for i in range(0,len(n_estimators)):
acclist.append(rfc_explor(n_estimators = n_estimators[i],
max_features = max_features[i],
max_depth = max_depth[i],
min_samples_split = min_samples_split[i],
min_samples_leaf = min_samples_leaf[i]
)
)
rfcdf = pd.DataFrame(pd.concat([pd.DataFrame({ "ModelVersion": "Top 15 Raw from PC",
"n_estimators": n_estimators,
"max_features": max_features,
"max_depth": max_depth,
"min_samples_split": min_samples_split,
"min_samples_leaf": min_samples_leaf
}),
pd.DataFrame(acclist)], axis = 1).reindex())
rfcdf.columns = ['ModelVersion', 'max_depth', 'max_features', 'min_samples_leaf','min_samples_split', 'n_estimators', 'Iteration 0', 'Iteration 1', 'Iteration 2', 'Iteration 3', 'Iteration 4', 'MeanAccuracy', 'RunTime']
display(rfcdf)
del rfcdf, acclist
### Model with PCA
acclist = []
n_estimators = [10 , 10 , 10 , 10 , 10 , 10 , 10 , 10 , 10 , 10 , 10 , 5 , 15 ]
max_features = ['auto', 'auto' , 'auto', 'auto', 'auto', 'auto', 'auto', 14 , 14 , 14 , 14 , 14 , 14 ]
max_depth = [None , None , None , None , None , None , None , None , 1000 , 500 , 100 , 1000 , 1000 ]
min_samples_split = [2 , 8 , 12 , 16 , 20 , 50 , 80 , 50 , 50 , 50 , 50 , 50 , 50 ]
min_samples_leaf = [1 , 4 , 6 , 8 , 10 , 25 , 40 , 25 , 25 , 25 , 25 , 25 , 25 ]
for i in range(0,len(n_estimators)):
acclist.append(rfc_explor_w_PCA(n_estimators = n_estimators[i],
max_features = max_features[i],
max_depth = max_depth[i],
min_samples_split = min_samples_split[i],
min_samples_leaf = min_samples_leaf[i],
PCA = PCA(n_components=22, svd_solver='randomized', random_state = seed)
)
)
rfcdf = pd.DataFrame(pd.concat([pd.DataFrame({ "ModelVersion": "With PCA",
"n_estimators": n_estimators,
"max_features": max_features,
"max_depth": max_depth,
"min_samples_split": min_samples_split,
"min_samples_leaf": min_samples_leaf
}),
pd.DataFrame(acclist)], axis = 1).reindex())
rfcdf.columns = ['ModelVersion', 'max_depth', 'max_features', 'min_samples_leaf','min_samples_split', 'n_estimators', 'Iteration 0', 'Iteration 1', 'Iteration 2', 'Iteration 3', 'Iteration 4', 'MeanAccuracy', 'RunTime']
display(rfcdf)
#'Iteration 5', 'Iteration 6', 'Iteration 7', 'Iteration 8', 'Iteration 9',
"""
CPU times: user 3 µs, sys: 1e+03 ns, total: 4 µs Wall time: 8.11 µs
'\nacclist = [] \nfullColumns = list(OPMAnalysisDataNoFam.columns)\n\nfor i in fullColumns:\n if i == "SEP": fullColumns.remove(i)\n\nn_estimators = [10 , 10 , 10 , 10 , 10 , 10 , 10 , 10 , 10 , 10 , 10 , 5 , 15 ] \nmax_features = [\'auto\', \'auto\' , \'auto\', \'auto\', \'auto\', \'auto\', \'auto\', 14 , 14 , 14 , 14 , 14 , 14 ] \nmax_depth = [None , None , None , None , None , None , None , None , 1000 , 500 , 100 , 1000 , 1000 ] \nmin_samples_split = [2 , 8 , 12 , 16 , 20 , 50 , 80 , 50 , 50 , 50 , 50 , 50 , 50 ] \nmin_samples_leaf = [1 , 4 , 6 , 8 , 10 , 25 , 40 , 25 , 25 , 25 , 25 , 25 , 25 ]\n\n##Model with all Raw Scaled Features\nfor i in range(0,len(n_estimators)):\n acclist.append(rfc_explor(n_estimators = n_estimators[i],\n max_features = max_features[i],\n max_depth = max_depth[i],\n min_samples_split = min_samples_split[i],\n min_samples_leaf = min_samples_leaf[i],\n cols = fullColumns\n )\n )\n\nrfcdf = pd.DataFrame(pd.concat([pd.DataFrame({ "ModelVersion": "All Raw Features",\n "n_estimators": n_estimators, \n "max_features": max_features, \n "max_depth": max_depth, \n "min_samples_split": min_samples_split,\n "min_samples_leaf": min_samples_leaf \n }),\n pd.DataFrame(acclist)], axis = 1).reindex())\nrfcdf.columns = [\'ModelVersion\', \'max_depth\', \'max_features\', \'min_samples_leaf\',\'min_samples_split\', \'n_estimators\', \'Iteration 0\', \'Iteration 1\', \'Iteration 2\', \'Iteration 3\', \'Iteration 4\', \'MeanAccuracy\', \'RunTime\']\ndisplay(rfcdf)\ndel rfcdf, acclist\n\nacclist = []\n\nn_estimators = [10 , 10 , 10 , 10 , 10 , 10 , 10 , 10 , 10 , 10 , 10 , 5 , 15 ] \nmax_features = [\'auto\', \'auto\' , \'auto\', \'auto\', \'auto\', \'auto\', \'auto\', 14 , 14 , 14 , 14 , 14 , 14 ] \nmax_depth = [None , None , None , None , None , None , None , None , 1000 , 500 , 100 , 1000 , 1000 ] \nmin_samples_split = [2 , 8 , 12 , 16 , 20 , 50 , 80 , 50 , 50 , 50 , 50 , 50 , 50 ] \nmin_samples_leaf = [1 , 4 , 6 , 8 , 10 , 25 , 40 , 25 , 25 , 25 , 25 , 25 , 25 ]\n\n## Model with only top 15 raw Scaled Principal Features \nfor i in range(0,len(n_estimators)):\n acclist.append(rfc_explor(n_estimators = n_estimators[i],\n max_features = max_features[i],\n max_depth = max_depth[i],\n min_samples_split = min_samples_split[i],\n min_samples_leaf = min_samples_leaf[i]\n )\n )\n\nrfcdf = pd.DataFrame(pd.concat([pd.DataFrame({ "ModelVersion": "Top 15 Raw from PC",\n "n_estimators": n_estimators, \n "max_features": max_features, \n "max_depth": max_depth, \n "min_samples_split": min_samples_split,\n "min_samples_leaf": min_samples_leaf \n }),\n pd.DataFrame(acclist)], axis = 1).reindex())\nrfcdf.columns = [\'ModelVersion\', \'max_depth\', \'max_features\', \'min_samples_leaf\',\'min_samples_split\', \'n_estimators\', \'Iteration 0\', \'Iteration 1\', \'Iteration 2\', \'Iteration 3\', \'Iteration 4\', \'MeanAccuracy\', \'RunTime\']\ndisplay(rfcdf)\ndel rfcdf, acclist\n\n### Model with PCA\nacclist = []\n\nn_estimators = [10 , 10 , 10 , 10 , 10 , 10 , 10 , 10 , 10 , 10 , 10 , 5 , 15 ] \nmax_features = [\'auto\', \'auto\' , \'auto\', \'auto\', \'auto\', \'auto\', \'auto\', 14 , 14 , 14 , 14 , 14 , 14 ] \nmax_depth = [None , None , None , None , None , None , None , None , 1000 , 500 , 100 , 1000 , 1000 ] \nmin_samples_split = [2 , 8 , 12 , 16 , 20 , 50 , 80 , 50 , 50 , 50 , 50 , 50 , 50 ] \nmin_samples_leaf = [1 , 4 , 6 , 8 , 10 , 25 , 40 , 25 , 25 , 25 , 25 , 25 , 25 ]\n\nfor i in range(0,len(n_estimators)):\n acclist.append(rfc_explor_w_PCA(n_estimators = n_estimators[i],\n max_features = max_features[i],\n max_depth = max_depth[i],\n min_samples_split = min_samples_split[i],\n min_samples_leaf = min_samples_leaf[i],\n PCA = PCA(n_components=22, svd_solver=\'randomized\', random_state = seed)\n )\n )\n\nrfcdf = pd.DataFrame(pd.concat([pd.DataFrame({ "ModelVersion": "With PCA",\n "n_estimators": n_estimators, \n "max_features": max_features, \n "max_depth": max_depth, \n "min_samples_split": min_samples_split,\n "min_samples_leaf": min_samples_leaf \n }),\n pd.DataFrame(acclist)], axis = 1).reindex())\nrfcdf.columns = [\'ModelVersion\', \'max_depth\', \'max_features\', \'min_samples_leaf\',\'min_samples_split\', \'n_estimators\', \'Iteration 0\', \'Iteration 1\', \'Iteration 2\', \'Iteration 3\', \'Iteration 4\', \'MeanAccuracy\', \'RunTime\']\ndisplay(rfcdf)\n\n#\'Iteration 5\', \'Iteration 6\', \'Iteration 7\', \'Iteration 8\', \'Iteration 9\', \n'
"""
def plot_confusion_matrix(cm, classes,
normalize=False,
title='Confusion matrix',
cmap=plt.cm.Blues):
#This function prints and plots the confusion matrix.
#Normalization can be applied by setting `normalize=True`.
plt.rcParams['figure.figsize'] = (18, 6)
plt.rcParams.update({'font.size': 16})
plt.rc('xtick', labelsize=18)
plt.rc('ytick', labelsize=18)
plt.imshow(cm, interpolation='nearest', cmap=cmap)
plt.title(title, fontsize = 18)
plt.colorbar()
tick_marks = np.arange(len(classes))
plt.xticks(tick_marks, classes, rotation=45)
plt.yticks(tick_marks, classes)
if normalize:
cm = cm.astype('float') / cm.sum(axis=1)[:, np.newaxis]
print("Normalized confusion matrix")
else:
print('Confusion matrix, without normalization')
print(cm)
thresh = cm.max() / 2.
for i, j in itertools.product(range(cm.shape[0]), range(cm.shape[1])):
plt.text(j, i, round(cm[i, j],2),
horizontalalignment="center",
color="white" if cm[i, j] > thresh else "black")
plt.tight_layout()
plt.ylabel('True label', fontsize = 18)
plt.xlabel('Predicted label', fontsize = 18)
plt.show()
"""
'\ndef plot_confusion_matrix(cm, classes,\n normalize=False,\n title=\'Confusion matrix\',\n cmap=plt.cm.Blues):\n \n #This function prints and plots the confusion matrix.\n #Normalization can be applied by setting `normalize=True`.\n \n plt.rcParams[\'figure.figsize\'] = (18, 6)\n plt.rcParams.update({\'font.size\': 16})\n plt.rc(\'xtick\', labelsize=18)\n plt.rc(\'ytick\', labelsize=18) \n plt.imshow(cm, interpolation=\'nearest\', cmap=cmap)\n plt.title(title, fontsize = 18)\n plt.colorbar()\n tick_marks = np.arange(len(classes))\n plt.xticks(tick_marks, classes, rotation=45)\n plt.yticks(tick_marks, classes)\n\n if normalize:\n cm = cm.astype(\'float\') / cm.sum(axis=1)[:, np.newaxis]\n print("Normalized confusion matrix")\n else:\n print(\'Confusion matrix, without normalization\')\n\n print(cm)\n\n thresh = cm.max() / 2.\n for i, j in itertools.product(range(cm.shape[0]), range(cm.shape[1])):\n plt.text(j, i, round(cm[i, j],2),\n horizontalalignment="center",\n color="white" if cm[i, j] > thresh else "black")\n\n plt.tight_layout()\n plt.ylabel(\'True label\', fontsize = 18)\n plt.xlabel(\'Predicted label\', fontsize = 18)\n\n plt.show()\n'
%%time
"""
def compute_kfold_scores_Classification( clf,
Data = OPMAnalysisDataNoFam,
cols = PCList,
cv = cv):
y = Data["SEP"].values # get the labels we want
y = np.where(y == 'NS', 0,
np.where(y == 'SA', 1,
np.where(y == 'SC', 2,
np.where(y == 'SD', 3,
np.where(y == 'SH', 4,
5
)
)
)
)
)
X = Data[cols].as_matrix()
# Run classifier with cross-validation and plot ROC curves
# setup pipeline to take PCA, then fit a clf model
clf_pipe = Pipeline(
[('minMaxScaler', MinMaxScaler()),
('CLF',clf)]
)
accuracy = []
#logloss = []
for (train, test), color in zip(cv.split(X, y), colors):
clf_pipe.fit(X[train],y[train]) # train object
y_hat = clf_pipe.predict(X[test]) # get test set preditions
a = float(mt.accuracy_score(y[test],y_hat))
#l = float(mt.log_loss(y[test], y_hat))
accuracy.append(round(a,5))
#logloss.append(round(l,5))
#print("Accuracy Ratings across all iterations: {0}\n\n\
#Average Accuracy: {1}\n\n\
#Log Loss Values across all iterations: {2}\n\n\
#Average Log Loss: {3}\n".format(accuracy, round(sum(accuracy)/len(accuracy),5), logloss,round(sum(logloss)/len(logloss),5)))
print("Accuracy Ratings across all iterations: {0}\n\n\
Average Accuracy: {1}\n".format(accuracy, round(sum(accuracy)/len(accuracy),5)))
ytestnames = np.where(y[test] == 0,'NS',
np.where(y[test] == 1,'SA',
np.where(y[test] == 2,'SC',
np.where(y[test] == 3,'SD',
np.where(y[test] == 4,'SH',
'SI'
)
)
)
)
)
yhatnames = np.where(y_hat == 0,'NS',
np.where(y_hat == 1,'SA',
np.where(y_hat == 2,'SC',
np.where(y_hat == 3,'SD',
np.where(y_hat == 4,'SH',
'SI'
)
)
)
)
)
#print(set(list(y_hat)))
print("confusion matrix\n{0}\n".format(pd.crosstab(ytestnames, yhatnames, rownames = ['True'], colnames = ['Predicted'], margins = True)))
# Plot non-normalized confusion matrix
plt.figure()
plot_confusion_matrix(confusion_matrix(y[test], y_hat),
classes =["NS", "SA", "SC", "SD", "SI"],
normalize =True,
title ='Confusion matrix, with normalization')
return clf_pipe.named_steps['CLF'], accuracy
"""
CPU times: user 3 µs, sys: 1e+03 ns, total: 4 µs Wall time: 8.34 µs
'\ndef compute_kfold_scores_Classification( clf,\n Data = OPMAnalysisDataNoFam,\n cols = PCList,\n cv = cv):\n\n y = Data["SEP"].values # get the labels we want \n \n y = np.where(y == \'NS\', 0, \n np.where(y == \'SA\', 1,\n np.where(y == \'SC\', 2,\n np.where(y == \'SD\', 3,\n np.where(y == \'SH\', 4,\n 5\n )\n )\n )\n )\n )\n \n X = Data[cols].as_matrix()\n\n\n # Run classifier with cross-validation and plot ROC curves\n\n # setup pipeline to take PCA, then fit a clf model\n clf_pipe = Pipeline(\n [(\'minMaxScaler\', MinMaxScaler()),\n (\'CLF\',clf)]\n )\n \n \n accuracy = []\n #logloss = []\n \n for (train, test), color in zip(cv.split(X, y), colors):\n clf_pipe.fit(X[train],y[train]) # train object\n y_hat = clf_pipe.predict(X[test]) # get test set preditions\n \n a = float(mt.accuracy_score(y[test],y_hat))\n #l = float(mt.log_loss(y[test], y_hat))\n \n accuracy.append(round(a,5)) \n\n #logloss.append(round(l,5)) \n \n #print("Accuracy Ratings across all iterations: {0}\n\n#Average Accuracy: {1}\n\n#Log Loss Values across all iterations: {2}\n\n#Average Log Loss: {3}\n".format(accuracy, round(sum(accuracy)/len(accuracy),5), logloss,round(sum(logloss)/len(logloss),5)))\n\n print("Accuracy Ratings across all iterations: {0}\n\nAverage Accuracy: {1}\n".format(accuracy, round(sum(accuracy)/len(accuracy),5)))\n\n \n ytestnames = np.where(y[test] == 0,\'NS\', \n np.where(y[test] == 1,\'SA\',\n np.where(y[test] == 2,\'SC\',\n np.where(y[test] == 3,\'SD\',\n np.where(y[test] == 4,\'SH\',\n \'SI\'\n )\n )\n )\n )\n )\n \n yhatnames = np.where(y_hat == 0,\'NS\', \n np.where(y_hat == 1,\'SA\',\n np.where(y_hat == 2,\'SC\',\n np.where(y_hat == 3,\'SD\',\n np.where(y_hat == 4,\'SH\',\n \'SI\'\n )\n )\n )\n )\n )\n #print(set(list(y_hat)))\n print("confusion matrix\n{0}\n".format(pd.crosstab(ytestnames, yhatnames, rownames = [\'True\'], colnames = [\'Predicted\'], margins = True)))\n \n # Plot non-normalized confusion matrix\n plt.figure()\n plot_confusion_matrix(confusion_matrix(y[test], y_hat), \n classes =["NS", "SA", "SC", "SD", "SI"], \n normalize =True,\n title =\'Confusion matrix, with normalization\')\n \n return clf_pipe.named_steps[\'CLF\'], accuracy\n'
%%time
"""
rfc_clf = RandomForestClassifier(n_estimators = 15,
max_features = 14,
max_depth = 1000.0,
min_samples_split = 50,
min_samples_leaf = 25,
class_weight = "balanced",
n_jobs = -1,
random_state = seed) # get object
rfc_clf, rfc_acc = compute_kfold_scores_Classification(rfc_clf, cols = fullColumns)
"""
CPU times: user 3 µs, sys: 1e+03 ns, total: 4 µs Wall time: 8.11 µs
'\nrfc_clf = RandomForestClassifier(n_estimators = 15, \n max_features = 14, \n max_depth = 1000.0, \n min_samples_split = 50, \n min_samples_leaf = 25, \n class_weight = "balanced",\n n_jobs = -1, \n random_state = seed) # get object\n \nrfc_clf, rfc_acc = compute_kfold_scores_Classification(rfc_clf, cols = fullColumns)\n'
"""
list(OPMAnalysisDataNoFam.SEP.unique())
"""
'\nlist(OPMAnalysisDataNoFam.SEP.unique())\n'
'''%%time
def compute_kfold_scores_Classification( clf,
Data = OPMAnalysisDataNoFam,
cols = PCList,
cv = cv):
y = Data["SEP"].values # get the labels we want
y = np.where(y == 'NS', 0,
np.where(y == 'SA', 1,
np.where(y == 'SC', 2,
np.where(y == 'SD', 3,
np.where(y == 'SH', 4,
5
)
)
)
)
)
X = Data[cols].as_matrix()
# Binarize the output
y_bin = label_binarize(Data["SEP"].values, list(Data.SEP.unique()))
n_classes = y_bin.shape[1]
# Run classifier with cross-validation and plot ROC curves
# setup pipeline to take PCA, then fit a clf model
clf_pipe = Pipeline(
[('minMaxScaler', MinMaxScaler()),
('CLF',clf)]
)
colors = cycle(['cyan', 'indigo', 'seagreen', 'yellow', 'blue', 'darkorange', 'pink', 'darkred', 'dimgray', 'maroon', 'coral'])
accuracy = []
#logloss = []
for (train, test), color in zip(cv.split(X, y), colors):
clf_pipe.fit(X[train],y[train]) # train object
y_hat = clf_pipe.predict(X[test]) # get test set preditions
a = float(mt.accuracy_score(y[test],y_hat))
#l = float(mt.log_loss(y[test], y_hat))
accuracy.append(round(a,5))
#logloss.append(round(l,5))
# Compute ROC curve and area the curve
#fpr, tpr, thresholds = roc_curve(y[test], probas_[:, 1])
#mean_tpr += interp(mean_fpr, fpr, tpr)
#mean_tpr[0] = 0.0
#roc_auc = auc(fpr, tpr)
#
#plt.rcParams['figure.figsize'] = (12, 6)
#
#plt.plot(fpr, tpr, lw=lw, color=color,
# label='ROC fold %d (area = %0.2f)' % (i, roc_auc))
#
#i += 1
# Compute ROC curve and ROC area for each class
fpr = dict()
tpr = dict()
roc_auc = dict()
for i in range(n_classes):
fpr[i], tpr[i], _ = roc_curve(y[test][:, i], y_hat[:, i])
roc_auc[i] = auc(fpr[i], tpr[i])
# Plot of a ROC curve for a specific class
plt.figure()
lw = 2
plt.plot(fpr[2], tpr[2], color='darkorange',
lw=lw, label='ROC curve (area = %0.2f)' % roc_auc[2])
plt.plot([0, 1], [0, 1], color='navy', lw=lw, linestyle='--')
plt.xlim([0.0, 1.0])
plt.ylim([0.0, 1.05])
plt.xlabel('False Positive Rate')
plt.ylabel('True Positive Rate')
plt.title('Receiver operating characteristic example')
plt.legend(loc="lower right")
plt.show()
#print("Accuracy Ratings across all iterations: {0}\n\n\
#Average Accuracy: {1}\n\n\
#Log Loss Values across all iterations: {2}\n\n\
#Average Log Loss: {3}\n".format(accuracy, round(sum(accuracy)/len(accuracy),5), logloss,round(sum(logloss)/len(logloss),5)))
print("Accuracy Ratings across all iterations: {0}\n\n\
Average Accuracy: {1}\n".format(accuracy, round(sum(accuracy)/len(accuracy),5)))
ytestnames = np.where(y[test] == 0,'NS',
np.where(y[test] == 1,'SA',
np.where(y[test] == 2,'SC',
np.where(y[test] == 3,'SD',
np.where(y[test] == 4,'SH',
'SI'
)
)
)
)
)
yhatnames = np.where(y_hat == 0,'NS',
np.where(y_hat == 1,'SA',
np.where(y_hat == 2,'SC',
np.where(y_hat == 3,'SD',
np.where(y_hat == 4,'SH',
'SI'
)
)
)
)
)
#print(set(list(y_hat)))
print("confusion matrix\n{0}\n".format(pd.crosstab(ytestnames, yhatnames, rownames = ['True'], colnames = ['Predicted'], margins = True)))
# Plot non-normalized confusion matrix
plt.figure()
plot_confusion_matrix(confusion_matrix(y[test], y_hat),
classes =["NS", "SA", "SC", "SD", "SH", "SI"],
normalize =True,
title ='Confusion matrix, with normalization')
return clf_pipe.named_steps['CLF'], accuracy'''
'%%time\n\ndef compute_kfold_scores_Classification( clf,\n Data = OPMAnalysisDataNoFam,\n cols = PCList,\n cv = cv):\n\n y = Data["SEP"].values # get the labels we want \n \n y = np.where(y == \'NS\', 0, \n np.where(y == \'SA\', 1,\n np.where(y == \'SC\', 2,\n np.where(y == \'SD\', 3,\n np.where(y == \'SH\', 4,\n 5\n )\n )\n )\n )\n )\n \n X = Data[cols].as_matrix()\n \n # Binarize the output\n y_bin = label_binarize(Data["SEP"].values, list(Data.SEP.unique()))\n n_classes = y_bin.shape[1]\n\n # Run classifier with cross-validation and plot ROC curves\n\n # setup pipeline to take PCA, then fit a clf model\n clf_pipe = Pipeline(\n [(\'minMaxScaler\', MinMaxScaler()),\n (\'CLF\',clf)]\n )\n \n colors = cycle([\'cyan\', \'indigo\', \'seagreen\', \'yellow\', \'blue\', \'darkorange\', \'pink\', \'darkred\', \'dimgray\', \'maroon\', \'coral\'])\n\n accuracy = []\n #logloss = []\n \n for (train, test), color in zip(cv.split(X, y), colors):\n clf_pipe.fit(X[train],y[train]) # train object\n y_hat = clf_pipe.predict(X[test]) # get test set preditions\n \n a = float(mt.accuracy_score(y[test],y_hat))\n #l = float(mt.log_loss(y[test], y_hat))\n \n accuracy.append(round(a,5)) \n\n #logloss.append(round(l,5))\n \n # Compute ROC curve and area the curve\n #fpr, tpr, thresholds = roc_curve(y[test], probas_[:, 1])\n #mean_tpr += interp(mean_fpr, fpr, tpr)\n #mean_tpr[0] = 0.0\n #roc_auc = auc(fpr, tpr)\n #\n #plt.rcParams[\'figure.figsize\'] = (12, 6)\n #\n #plt.plot(fpr, tpr, lw=lw, color=color,\n # label=\'ROC fold %d (area = %0.2f)\' % (i, roc_auc))\n#\n #i += 1\n # Compute ROC curve and ROC area for each class\n fpr = dict()\n tpr = dict()\n roc_auc = dict()\n for i in range(n_classes):\n fpr[i], tpr[i], _ = roc_curve(y[test][:, i], y_hat[:, i])\n roc_auc[i] = auc(fpr[i], tpr[i])\n\n # Plot of a ROC curve for a specific class\n plt.figure()\n lw = 2\n plt.plot(fpr[2], tpr[2], color=\'darkorange\',\n lw=lw, label=\'ROC curve (area = %0.2f)\' % roc_auc[2])\n plt.plot([0, 1], [0, 1], color=\'navy\', lw=lw, linestyle=\'--\')\n plt.xlim([0.0, 1.0])\n plt.ylim([0.0, 1.05])\n plt.xlabel(\'False Positive Rate\')\n plt.ylabel(\'True Positive Rate\')\n plt.title(\'Receiver operating characteristic example\')\n plt.legend(loc="lower right")\n plt.show()\n #print("Accuracy Ratings across all iterations: {0}\n\n#Average Accuracy: {1}\n\n#Log Loss Values across all iterations: {2}\n\n#Average Log Loss: {3}\n".format(accuracy, round(sum(accuracy)/len(accuracy),5), logloss,round(sum(logloss)/len(logloss),5)))\n\n print("Accuracy Ratings across all iterations: {0}\n\nAverage Accuracy: {1}\n".format(accuracy, round(sum(accuracy)/len(accuracy),5)))\n\n \n ytestnames = np.where(y[test] == 0,\'NS\', \n np.where(y[test] == 1,\'SA\',\n np.where(y[test] == 2,\'SC\',\n np.where(y[test] == 3,\'SD\',\n np.where(y[test] == 4,\'SH\',\n \'SI\'\n )\n )\n )\n )\n )\n \n yhatnames = np.where(y_hat == 0,\'NS\', \n np.where(y_hat == 1,\'SA\',\n np.where(y_hat == 2,\'SC\',\n np.where(y_hat == 3,\'SD\',\n np.where(y_hat == 4,\'SH\',\n \'SI\'\n )\n )\n )\n )\n )\n #print(set(list(y_hat)))\n print("confusion matrix\n{0}\n".format(pd.crosstab(ytestnames, yhatnames, rownames = [\'True\'], colnames = [\'Predicted\'], margins = True)))\n \n # Plot non-normalized confusion matrix\n plt.figure()\n plot_confusion_matrix(confusion_matrix(y[test], y_hat), \n classes =["NS", "SA", "SC", "SD", "SH", "SI"], \n normalize =True,\n title =\'Confusion matrix, with normalization\')\n \n return clf_pipe.named_steps[\'CLF\'], accuracy'
'''%%time
rfc_clf = OneVsRestClassifier(RandomForestClassifier(n_estimators = 15,
max_features = 14,
max_depth = 1000.0,
min_samples_split = 50,
min_samples_leaf = 25,
class_weight = "balanced",
n_jobs = -1,
random_state = seed)) # get object
rfc_clf, rfc_acc = compute_kfold_scores_Classification(rfc_clf, cols = fullColumns)'''
'%%time\n\nrfc_clf = OneVsRestClassifier(RandomForestClassifier(n_estimators = 15, \n max_features = 14, \n max_depth = 1000.0, \n min_samples_split = 50, \n min_samples_leaf = 25, \n class_weight = "balanced",\n n_jobs = -1, \n random_state = seed)) # get object\n \nrfc_clf, rfc_acc = compute_kfold_scores_Classification(rfc_clf, cols = fullColumns)'
'''%%time
rfc_clf = OneVsRestClassifier(RandomForestClassifier(n_estimators = 15,
max_features = 14,
max_depth = 1000.0,
min_samples_split = 50,
min_samples_leaf = 25,
class_weight = "balanced",
n_jobs = -1,
random_state = seed)) # get object
y = OPMAnalysisDataNoFam["SEP"].values # get the labels we want
y = np.where(y == 'NS', 0,
np.where(y == 'SA', 1,
np.where(y == 'SC', 2,
np.where(y == 'SD', 3,
np.where(y == 'SH', 4,
5
)
)
)
)
)
X = OPMAnalysisDataNoFam[fullColumns].as_matrix()
# Binarize the output
#y_bin = label_binarize(OPMAnalysisDataNoFam["SEP"].values, list(OPMAnalysisDataNoFam.SEP.unique()))
n_classes = len(set(y))
#classifier = OneVsRestClassifier(svm.SVC(kernel='linear', probability=True,
# random_state=random_state))
#y_score = rfc_clf.fit(X[train], y[train]).decision_function(X_test)
#
colors = cycle(['cyan', 'indigo', 'seagreen', 'yellow', 'blue', 'darkorange', 'pink', 'darkred', 'dimgray', 'maroon', 'coral'])
#
#accuracy = []
#
#for (train, test), color in zip(cv.split(X, y), colors):
# probas_ = rfc_clf.fit(X[train], y[train]).predict_proba(X[test])
#
# #rfc_clf.fit(X[train],y[train]) # train object
# y_hat = rfc_clf.predict(X[test]) # get test set preditions
# #y_hat = rfc_clf.fit(X[train],y[train]).decision_function(X[test])
#
# fpr = dict()
# tpr = dict()
# roc_auc = dict()
# for i in range(n_classes):
# #fpr[i], tpr[i], _ = roc_curve(y[test][:, i], y_hat[:, i])
# print(len(probas_[:, i]))
# print(y[test])
# #fpr[i], tpr[i], thresholds = roc_curve(y[test][:, i], probas_[:, i])
# #roc_auc[i] = auc(fpr[i], tpr[i])
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=.5, random_state=seed)
probas_ = rfc_clf.fit(X_train, y_train).predict_proba(X_test)
#rfc_clf.fit(X[train],y[train]) # train object
#y_hat = rfc_clf.predict(X_test) # get test set preditions
#y_hat = rfc_clf.fit(X_train,y_train).decision_function(X_test)
fpr = dict()
tpr = dict()
roc_auc = dict()
for i in range(n_classes):
#fpr[i], tpr[i], _ = roc_curve(y[test][:, i], y_hat[:, i])
print(probas_[:, i])
print(y_test)
#fpr[i], tpr[i], thresholds = roc_curve(y[test][i], probas_[:, i])
#roc_auc[i] = auc(fpr[i], tpr[i])'''
'%%time\n\nrfc_clf = OneVsRestClassifier(RandomForestClassifier(n_estimators = 15, \n max_features = 14, \n max_depth = 1000.0, \n min_samples_split = 50, \n min_samples_leaf = 25, \n class_weight = "balanced",\n n_jobs = -1, \n random_state = seed)) # get object\n\ny = OPMAnalysisDataNoFam["SEP"].values # get the labels we want\n \ny = np.where(y == \'NS\', 0, \n np.where(y == \'SA\', 1,\n np.where(y == \'SC\', 2,\n np.where(y == \'SD\', 3,\n np.where(y == \'SH\', 4,\n 5\n )\n )\n )\n )\n )\n\nX = OPMAnalysisDataNoFam[fullColumns].as_matrix()\n\n# Binarize the output\n#y_bin = label_binarize(OPMAnalysisDataNoFam["SEP"].values, list(OPMAnalysisDataNoFam.SEP.unique()))\nn_classes = len(set(y))\n\n#classifier = OneVsRestClassifier(svm.SVC(kernel=\'linear\', probability=True,\n# random_state=random_state))\n#y_score = rfc_clf.fit(X[train], y[train]).decision_function(X_test)\n#\ncolors = cycle([\'cyan\', \'indigo\', \'seagreen\', \'yellow\', \'blue\', \'darkorange\', \'pink\', \'darkred\', \'dimgray\', \'maroon\', \'coral\'])\n#\n#accuracy = []\n#\n\n#for (train, test), color in zip(cv.split(X, y), colors):\n# probas_ = rfc_clf.fit(X[train], y[train]).predict_proba(X[test]) \n# \n# #rfc_clf.fit(X[train],y[train]) # train object\n# y_hat = rfc_clf.predict(X[test]) # get test set preditions\n# #y_hat = rfc_clf.fit(X[train],y[train]).decision_function(X[test])\n# \n# fpr = dict()\n# tpr = dict()\n# roc_auc = dict()\n# for i in range(n_classes):\n# #fpr[i], tpr[i], _ = roc_curve(y[test][:, i], y_hat[:, i])\n# print(len(probas_[:, i]))\n# print(y[test])\n# #fpr[i], tpr[i], thresholds = roc_curve(y[test][:, i], probas_[:, i])\n# #roc_auc[i] = auc(fpr[i], tpr[i])\n\nX_train, X_test, y_train, y_test = train_test_split(X, y, test_size=.5, random_state=seed)\n\nprobas_ = rfc_clf.fit(X_train, y_train).predict_proba(X_test) \n \n#rfc_clf.fit(X[train],y[train]) # train object\n#y_hat = rfc_clf.predict(X_test) # get test set preditions\n#y_hat = rfc_clf.fit(X_train,y_train).decision_function(X_test)\n\nfpr = dict()\ntpr = dict()\nroc_auc = dict()\nfor i in range(n_classes):\n #fpr[i], tpr[i], _ = roc_curve(y[test][:, i], y_hat[:, i])\n print(probas_[:, i])\n print(y_test)\n #fpr[i], tpr[i], thresholds = roc_curve(y[test][i], probas_[:, i])\n #roc_auc[i] = auc(fpr[i], tpr[i])'
As was done before, we assess weights across classes. Since stratification was performed previously, we have equal weights. Thus, we can ignore weighting in our binary classifications.
OPMClassWeights = class_weight.compute_class_weight("balanced", OPMAnalysisDataNoFamBinary["SEP"].drop_duplicates(), OPMAnalysisDataNoFamBinary["SEP"])
display(stratumProf.merge(pd.DataFrame({"Weight": OPMClassWeights, "SEP": OPMAnalysisDataNoFamBinary["SEP"].drop_duplicates()}),on="SEP", how="inner"))
| SEP | StratCount | StratCountSample | Weight | |
|---|---|---|---|---|
| 0 | NS | 21076 | 7500.0 | 0.995065 |
| 1 | SC | 7423 | 7423.0 | 1.004985 |
We have chosen to utilize Stratified KFold Cross Validation for our classification analysis, with 5 folds. This means, that from our original sample size of 8002, each "fold" will save off approximately 20% as test observations utilizing the rest as training observations all while keeping the ratio of classes equal amongst customers and subscribers. This process will occur through 5 iterations, or folds, to allow us to cross validate our results amongst different test/train combinations. We have utilized a random_state seed equal to the length of the original sampled dataset to ensure reproducible results.
seed = len(OPMAnalysisDataNoFamBinary)
cv = StratifiedKFold(n_splits = 5, random_state = seed)
print(OPMAnalysisDataNoFamBinary.shape)
print(cv)
(14920, 100) StratifiedKFold(n_splits=5, random_state=14920, shuffle=False)
Max Depth The maximum depth (levels) in the tree. When a value is set, the tree may not split further once this level has been met regardless of how many nodes are in the leaf.
Max Features Number of features to consider when looking for a split.
Minimum Samples in Leaf Minimum number of samples required to be in a leaf node. Splits may not occur which cause the number of samples in a leaf to be less than this value. Too low a value here leads to overfitting the tree to train data.
Minimum Samples to Split Minimum number fo samples required to split a node. Care was taken during parameter tests to keep the ratio between Min Samples in Leaf and Min Samples to Split equal to that of the default values (1:2). This was done to allow an even 50/50 split on nodes which match the lowest granularity split criteria. similar to the min samples in leaf, too low a value here leads to overfitting the tree to train data.
n_estimators Number of Trees generated in the forest. Increasing the number of trees, in our models increased accuracy while decreasing performance. We tuned to provide output that completed all 10 iterations in under 10 minutes.
| max_depth | max_features | min_samples_leaf | min_samples_split | n_estimators |
|---|---|---|---|---|
| TBD | TBD | TBD | TBD | TBD |
%%time
def rfc_explorBinary(n_estimators,
max_features,
max_depth,
min_samples_split,
min_samples_leaf,
Data = OPMAnalysisDataNoFamBinary,
cols = PCList,
cv = cv,
seed = seed):
startTime = datetime.now()
y = Data["SEP"].values # get the labels we want
X = Data[cols].as_matrix()
rfc_clf = RandomForestClassifier(n_estimators=n_estimators, max_features = max_features, max_depth=max_depth, min_samples_split = min_samples_split, min_samples_leaf = min_samples_leaf, n_jobs=-1, random_state = seed) # get object
# setup pipeline to take PCA, then fit a clf model
clf_pipe = Pipeline(
[('minMaxScaler', MinMaxScaler()),
('CLF',rfc_clf)]
)
accuracy = cross_val_score(clf_pipe, X, y, cv=cv.split(X, y)) # this also can help with parallelism
MeanAccuracy = sum(accuracy)/len(accuracy)
accuracy = np.append(accuracy, MeanAccuracy)
endTime = datetime.now()
TotalTime = endTime - startTime
accuracy = np.append(accuracy, TotalTime)
#print(TotalTime)
#print(accuracy)
return accuracy
CPU times: user 5 µs, sys: 0 ns, total: 5 µs Wall time: 9.54 µs
%%time
def rfc_explorBinary_w_PCA(n_estimators,
max_features,
max_depth,
min_samples_split,
min_samples_leaf,
PCA,
Data = OPMAnalysisDataNoFamBinary,
cv = cv,
seed = seed):
startTime = datetime.now()
y = Data["SEP"].values # get the labels we want
X = Data.drop("SEP", axis=1).as_matrix()
rfc_clf = RandomForestClassifier(n_estimators=n_estimators, max_features = max_features, max_depth=max_depth, min_samples_split = min_samples_split, min_samples_leaf = min_samples_leaf, n_jobs=-1, random_state = seed) # get object
# setup pipeline to take PCA, then fit a clf model
clf_pipe = Pipeline(
[('minMaxScaler', MinMaxScaler()),
('PCA', PCA),
('CLF',rfc_clf)]
)
accuracy = cross_val_score(clf_pipe, X, y, cv=cv.split(X, y)) # this also can help with parallelism
MeanAccuracy = sum(accuracy)/len(accuracy)
accuracy = np.append(accuracy, MeanAccuracy)
endTime = datetime.now()
TotalTime = endTime - startTime
accuracy = np.append(accuracy, TotalTime)
#print(TotalTime)
#print(accuracy)
return accuracy
CPU times: user 4 µs, sys: 2 µs, total: 6 µs Wall time: 9.3 µs
%%time
FinalResultsDF = pd.DataFrame(columns= ['ModelVersion', 'Iteration 0', 'Iteration 1', 'Iteration 2', 'Iteration 3', 'Iteration 4', 'MeanAccuracy'])
TopResultsDF = pd.DataFrame(columns= ['ModelVersion', 'Iteration 0', 'Iteration 1', 'Iteration 2', 'Iteration 3', 'Iteration 4', 'MeanAccuracy'])
acclist = []
fullColumns = list(OPMAnalysisDataNoFamBinary.columns)
for i in fullColumns:
if i == "SEP": fullColumns.remove(i)
n_estimators = [10 , 10 , 10 , 10 , 10 , 10 , 10 , 10 , 10 , 10 , 10 , 10 , 10 , 10 , 10 , 10 , 10 , 10 , 10 , 10 , 10 , 10 , 10 , 10 , 15 , 20 , 30 , 50 ]
max_features = ['auto', 'auto' , 'auto', 'auto', 'auto', 'auto', 'auto', 'auto', 5 , 10 , 15 , 20 , None , 15 , 15 , 15 , 15 , 15 , 15 , 15 , 15 , 15 , 15 , 15 , 15 , 15 , 15 , 15 ]
max_depth = [None , None , None , None , None , None , None , None , None , None , None , None , None , 10 , 15 , 20 , 25 , 30 , 17 , 18 , 19 , 21 , 22 , 23 , 15 , 15 , 15 , 15 ]
min_samples_split = [2 , 8 , 12 , 18 , 20 , 24 , 36 , 48 , 36 , 36 , 36 , 36 , 36 , 36 , 36 , 36 , 36 , 36 , 36 , 36 , 36 , 36 , 36 , 36 , 36 , 36 , 36 , 36 ]
min_samples_leaf = [1 , 4 , 6 , 9 , 10 , 12 , 18 , 24 , 18 , 18 , 18 , 18 , 18 , 18 , 18 , 18 , 18 , 18 , 18 , 18 , 18 , 18 , 18 , 18 , 18 , 18 , 18 , 18 ]
##Model with all Raw Scaled Features
for i in range(0,len(n_estimators)):
acclist.append(rfc_explorBinary(n_estimators = n_estimators[i],
max_features = max_features[i],
max_depth = max_depth[i],
min_samples_split = min_samples_split[i],
min_samples_leaf = min_samples_leaf[i],
cols = fullColumns
)
)
rfcdf = pd.DataFrame(pd.concat([pd.DataFrame({ "ModelVersion": "Random Forest: All Raw Features",
"n_estimators": n_estimators,
"max_features": max_features,
"max_depth": max_depth,
"min_samples_split": min_samples_split,
"min_samples_leaf": min_samples_leaf
}),
pd.DataFrame(acclist)], axis = 1).reindex())
rfcdf.columns = ['ModelVersion', 'max_depth', 'max_features', 'min_samples_leaf','min_samples_split', 'n_estimators', 'Iteration 0', 'Iteration 1', 'Iteration 2', 'Iteration 3', 'Iteration 4', 'MeanAccuracy', 'RunTime']
display(rfcdf)
TopResultsDF = pd.concat([TopResultsDF, rfcdf.sort_values(['MeanAccuracy'], ascending=False)[TopResultsDF.columns].head(1)]).sort_values(['MeanAccuracy'], ascending=False).reset_index(drop=True)
del rfcdf, acclist
acclist = []
n_estimators = [10 , 10 , 10 , 10 , 10 , 10 , 10 , 10 , 10 , 10 , 10 , 10 , 10 , 10 , 10 , 10 , 10 , 10 , 10 , 10 , 10 , 10 , 10 , 10 , 15 , 20 , 30 , 50 ]
max_features = ['auto', 'auto' , 'auto', 'auto', 'auto', 'auto', 'auto', 'auto', 5 , 10 , 15 , 20 , None , 'auto', 'auto', 'auto', 'auto', 'auto', 'auto', 'auto', 'auto', 'auto', 'auto', 'auto', 'auto', 'auto', 'auto', 'auto']
max_depth = [None , None , None , None , None , None , None , None , None , None , None , None , None , 10 , 15 , 20 , 25 , 30 , 17 , 18 , 19 , 21 , 22 , 23 , 17 , 17 , 17 , 17 ]
min_samples_split = [2 , 8 , 12 , 18 , 20 , 24 , 36 , 48 , 48 , 48 , 48 , 48 , 48 , 48 , 48 , 48 , 48 , 48 , 48 , 48 , 48 , 48 , 48 , 48 , 48 , 48 , 48 , 48 ]
min_samples_leaf = [1 , 4 , 6 , 9 , 10 , 12 , 18 , 24 , 24 , 24 , 24 , 24 , 24 , 24 , 24 , 24 , 24 , 24 , 24 , 24 , 24 , 24 , 24 , 24 , 24 , 24 , 24 , 24 ]
## Model with only top 15 raw Scaled Principal Features
for i in range(0,len(n_estimators)):
acclist.append(rfc_explorBinary(n_estimators = n_estimators[i],
max_features = max_features[i],
max_depth = max_depth[i],
min_samples_split = min_samples_split[i],
min_samples_leaf = min_samples_leaf[i]
)
)
rfcdf = pd.DataFrame(pd.concat([pd.DataFrame({ "ModelVersion": "Random Forest: Top 15 Raw from PC",
"n_estimators": n_estimators,
"max_features": max_features,
"max_depth": max_depth,
"min_samples_split": min_samples_split,
"min_samples_leaf": min_samples_leaf
}),
pd.DataFrame(acclist)], axis = 1).reindex())
rfcdf.columns = ['ModelVersion', 'max_depth', 'max_features', 'min_samples_leaf','min_samples_split', 'n_estimators', 'Iteration 0', 'Iteration 1', 'Iteration 2', 'Iteration 3', 'Iteration 4', 'MeanAccuracy', 'RunTime']
display(rfcdf)
TopResultsDF = pd.concat([TopResultsDF, rfcdf.sort_values(['MeanAccuracy'], ascending=False)[TopResultsDF.columns].head(1)]).sort_values(['MeanAccuracy'], ascending=False).reset_index(drop=True)
del rfcdf, acclist
### Model with PCA
acclist = []
n_estimators = [10 , 10 , 10 , 10 , 10 , 10 , 10 , 10 , 10 , 10 , 10 , 10 , 10 , 10 , 10 , 10 , 10 , 10 , 10 , 10 , 10 , 10 , 10 , 15 , 20 , 30 , 50 ]
max_features = ['auto', 'auto' , 'auto', 'auto', 'auto', 'auto', 'auto', 5 , 10 , 15 , 20 , None , 'auto', 'auto', 'auto', 'auto', 'auto', 'auto', 'auto', 'auto', 'auto', 'auto', 'auto', 'auto', 'auto', 'auto', 'auto']
max_depth = [None , None , None , None , None , None , None , None , None , None , None , None , 10 , 15 , 20 , 25 , 30 , 17 , 18 , 19 , 21 , 22 , 23 , 15 , 15 , 15 , 15 ]
min_samples_split = [2 , 8 , 12 , 18 , 20 , 24 , 36 , 24 , 24 , 24 , 24 , 24 , 24 , 24 , 24 , 24 , 24 , 24 , 24 , 24 , 24 , 24 , 24 , 24 , 24 , 24 , 24 ]
min_samples_leaf = [1 , 4 , 6 , 9 , 10 , 12 , 18 , 12 , 12 , 12 , 12 , 12 , 12 , 12 , 12 , 12 , 12 , 12 , 12 , 12 , 12 , 12 , 12 , 12 , 12 , 12 , 12 ]
for i in range(0,len(n_estimators)):
acclist.append(rfc_explorBinary_w_PCA(n_estimators = n_estimators[i],
max_features = max_features[i],
max_depth = max_depth[i],
min_samples_split = min_samples_split[i],
min_samples_leaf = min_samples_leaf[i],
PCA = PCA(n_components=22, svd_solver='randomized', random_state = seed)
)
)
rfcdf = pd.DataFrame(pd.concat([pd.DataFrame({ "ModelVersion": "Random Forest: With PCA",
"n_estimators": n_estimators,
"max_features": max_features,
"max_depth": max_depth,
"min_samples_split": min_samples_split,
"min_samples_leaf": min_samples_leaf
}),
pd.DataFrame(acclist)], axis = 1).reindex())
rfcdf.columns = ['ModelVersion', 'max_depth', 'max_features', 'min_samples_leaf','min_samples_split', 'n_estimators', 'Iteration 0', 'Iteration 1', 'Iteration 2', 'Iteration 3', 'Iteration 4', 'MeanAccuracy', 'RunTime']
display(rfcdf)
TopResultsDF = pd.concat([TopResultsDF, rfcdf.sort_values(['MeanAccuracy'], ascending=False)[TopResultsDF.columns].head(1)]).sort_values(['MeanAccuracy'], ascending=False).reset_index(drop=True)
del rfcdf, acclist
| ModelVersion | max_depth | max_features | min_samples_leaf | min_samples_split | n_estimators | Iteration 0 | Iteration 1 | Iteration 2 | Iteration 3 | Iteration 4 | MeanAccuracy | RunTime | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | Random Forest: All Raw Features | NaN | auto | 1 | 2 | 10 | 0.597320 | 0.697822 | 0.714477 | 0.739524 | 0.694268 | 0.688682 | 00:00:01.413823 |
| 1 | Random Forest: All Raw Features | NaN | auto | 4 | 8 | 10 | 0.628811 | 0.706868 | 0.717493 | 0.755615 | 0.713041 | 0.704365 | 00:00:01.437996 |
| 2 | Random Forest: All Raw Features | NaN | auto | 6 | 12 | 10 | 0.605360 | 0.715578 | 0.717828 | 0.735836 | 0.725444 | 0.700009 | 00:00:01.436993 |
| 3 | Random Forest: All Raw Features | NaN | auto | 9 | 18 | 10 | 0.640536 | 0.702178 | 0.726877 | 0.756621 | 0.721086 | 0.709459 | 00:00:01.441244 |
| 4 | Random Forest: All Raw Features | NaN | auto | 10 | 20 | 10 | 0.581910 | 0.701508 | 0.731233 | 0.766678 | 0.723768 | 0.701019 | 00:00:01.439517 |
| 5 | Random Forest: All Raw Features | NaN | auto | 12 | 24 | 10 | 0.623451 | 0.686432 | 0.720509 | 0.769695 | 0.715387 | 0.703095 | 00:00:01.436341 |
| 6 | Random Forest: All Raw Features | NaN | auto | 18 | 36 | 10 | 0.660302 | 0.712898 | 0.714142 | 0.753269 | 0.722427 | 0.712607 | 00:00:01.436919 |
| 7 | Random Forest: All Raw Features | NaN | auto | 24 | 48 | 10 | 0.619430 | 0.719598 | 0.714142 | 0.740530 | 0.715387 | 0.701817 | 00:00:01.434429 |
| 8 | Random Forest: All Raw Features | NaN | 5 | 18 | 36 | 10 | 0.655946 | 0.709883 | 0.711796 | 0.732819 | 0.714381 | 0.704965 | 00:00:01.426460 |
| 9 | Random Forest: All Raw Features | NaN | 10 | 18 | 36 | 10 | 0.682412 | 0.695812 | 0.715147 | 0.748910 | 0.732819 | 0.715020 | 00:00:01.432464 |
| 10 | Random Forest: All Raw Features | NaN | 15 | 18 | 36 | 10 | 0.625461 | 0.726633 | 0.725201 | 0.768019 | 0.734160 | 0.715895 | 00:00:01.436789 |
| 11 | Random Forest: All Raw Features | NaN | 20 | 18 | 36 | 10 | 0.617085 | 0.730988 | 0.706434 | 0.768689 | 0.707006 | 0.706041 | 00:00:01.434392 |
| 12 | Random Forest: All Raw Features | NaN | None | 18 | 36 | 10 | 0.611390 | 0.725293 | 0.725536 | 0.767684 | 0.697955 | 0.705572 | 00:00:01.938432 |
| 13 | Random Forest: All Raw Features | 10.0 | 15 | 18 | 36 | 10 | 0.636181 | 0.721608 | 0.694370 | 0.755950 | 0.728461 | 0.707314 | 00:00:01.440936 |
| 14 | Random Forest: All Raw Features | 15.0 | 15 | 18 | 36 | 10 | 0.651926 | 0.725628 | 0.718834 | 0.757291 | 0.738183 | 0.718373 | 00:00:01.437259 |
| 15 | Random Forest: All Raw Features | 20.0 | 15 | 18 | 36 | 10 | 0.625461 | 0.726633 | 0.725201 | 0.767684 | 0.734160 | 0.715828 | 00:00:01.434720 |
| 16 | Random Forest: All Raw Features | 25.0 | 15 | 18 | 36 | 10 | 0.625461 | 0.726633 | 0.725201 | 0.768019 | 0.734160 | 0.715895 | 00:00:01.436556 |
| 17 | Random Forest: All Raw Features | 30.0 | 15 | 18 | 36 | 10 | 0.625461 | 0.726633 | 0.725201 | 0.768019 | 0.734160 | 0.715895 | 00:00:01.437644 |
| 18 | Random Forest: All Raw Features | 17.0 | 15 | 18 | 36 | 10 | 0.617420 | 0.717588 | 0.725201 | 0.763996 | 0.734831 | 0.711807 | 00:00:01.435784 |
| 19 | Random Forest: All Raw Features | 18.0 | 15 | 18 | 36 | 10 | 0.651926 | 0.711223 | 0.725201 | 0.764666 | 0.735166 | 0.717637 | 00:00:01.435802 |
| 20 | Random Forest: All Raw Features | 19.0 | 15 | 18 | 36 | 10 | 0.638526 | 0.721273 | 0.725201 | 0.766678 | 0.734160 | 0.717168 | 00:00:01.436183 |
| 21 | Random Forest: All Raw Features | 21.0 | 15 | 18 | 36 | 10 | 0.625461 | 0.726633 | 0.725201 | 0.768019 | 0.734160 | 0.715895 | 00:00:01.439370 |
| 22 | Random Forest: All Raw Features | 22.0 | 15 | 18 | 36 | 10 | 0.625461 | 0.726633 | 0.725201 | 0.768019 | 0.734160 | 0.715895 | 00:00:01.436523 |
| 23 | Random Forest: All Raw Features | 23.0 | 15 | 18 | 36 | 10 | 0.625461 | 0.726633 | 0.725201 | 0.768019 | 0.734160 | 0.715895 | 00:00:01.436642 |
| 24 | Random Forest: All Raw Features | 15.0 | 15 | 18 | 36 | 15 | 0.649246 | 0.727973 | 0.712802 | 0.758297 | 0.735166 | 0.716697 | 00:00:01.468255 |
| 25 | Random Forest: All Raw Features | 15.0 | 15 | 18 | 36 | 20 | 0.629146 | 0.715243 | 0.718164 | 0.760644 | 0.729132 | 0.710466 | 00:00:01.503438 |
| 26 | Random Forest: All Raw Features | 15.0 | 15 | 18 | 36 | 30 | 0.623451 | 0.702848 | 0.720509 | 0.762320 | 0.728126 | 0.707451 | 00:00:01.557565 |
| 27 | Random Forest: All Raw Features | 15.0 | 15 | 18 | 36 | 50 | 0.643886 | 0.714908 | 0.722855 | 0.762655 | 0.726450 | 0.714151 | 00:00:02.196062 |
| ModelVersion | max_depth | max_features | min_samples_leaf | min_samples_split | n_estimators | Iteration 0 | Iteration 1 | Iteration 2 | Iteration 3 | Iteration 4 | MeanAccuracy | RunTime | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | Random Forest: Top 15 Raw from PC | NaN | auto | 1 | 2 | 10 | 0.556449 | 0.682412 | 0.732574 | 0.743547 | 0.710694 | 0.685135 | 00:00:01.366742 |
| 1 | Random Forest: Top 15 Raw from PC | NaN | auto | 4 | 8 | 10 | 0.591625 | 0.694137 | 0.730563 | 0.749916 | 0.730808 | 0.699410 | 00:00:01.374599 |
| 2 | Random Forest: Top 15 Raw from PC | NaN | auto | 6 | 12 | 10 | 0.571859 | 0.697487 | 0.698056 | 0.769024 | 0.723433 | 0.691972 | 00:00:01.378227 |
| 3 | Random Forest: Top 15 Raw from PC | NaN | auto | 9 | 18 | 10 | 0.595980 | 0.697822 | 0.737936 | 0.767684 | 0.737848 | 0.707454 | 00:00:01.376594 |
| 4 | Random Forest: Top 15 Raw from PC | NaN | auto | 10 | 20 | 10 | 0.605025 | 0.714573 | 0.732239 | 0.760979 | 0.723433 | 0.707250 | 00:00:01.381793 |
| 5 | Random Forest: Top 15 Raw from PC | NaN | auto | 12 | 24 | 10 | 0.634841 | 0.709548 | 0.728217 | 0.756621 | 0.733155 | 0.712476 | 00:00:01.378621 |
| 6 | Random Forest: Top 15 Raw from PC | NaN | auto | 18 | 36 | 10 | 0.649246 | 0.712898 | 0.717493 | 0.759638 | 0.731143 | 0.714084 | 00:00:01.377813 |
| 7 | Random Forest: Top 15 Raw from PC | NaN | auto | 24 | 48 | 10 | 0.639866 | 0.721608 | 0.724196 | 0.752263 | 0.743882 | 0.716363 | 00:00:01.375368 |
| 8 | Random Forest: Top 15 Raw from PC | NaN | 5 | 24 | 48 | 10 | 0.645226 | 0.717588 | 0.714477 | 0.746229 | 0.714717 | 0.707647 | 00:00:01.376184 |
| 9 | Random Forest: Top 15 Raw from PC | NaN | 10 | 24 | 48 | 10 | 0.607705 | 0.706868 | 0.731568 | 0.759638 | 0.730808 | 0.707317 | 00:00:01.375668 |
| 10 | Random Forest: Top 15 Raw from PC | NaN | 15 | 24 | 48 | 10 | 0.637521 | 0.708543 | 0.726542 | 0.767684 | 0.716728 | 0.711403 | 00:00:01.379999 |
| 11 | Random Forest: Top 15 Raw from PC | NaN | 20 | 24 | 48 | 10 | 0.613065 | 0.730653 | 0.716823 | 0.772377 | 0.705665 | 0.707717 | 00:00:01.376902 |
| 12 | Random Forest: Top 15 Raw from PC | NaN | None | 24 | 48 | 10 | 0.608710 | 0.744724 | 0.715483 | 0.760979 | 0.716058 | 0.709191 | 00:00:01.584239 |
| 13 | Random Forest: Top 15 Raw from PC | 10.0 | auto | 24 | 48 | 10 | 0.630486 | 0.712228 | 0.722185 | 0.755950 | 0.722092 | 0.708588 | 00:00:01.379572 |
| 14 | Random Forest: Top 15 Raw from PC | 15.0 | auto | 24 | 48 | 10 | 0.621441 | 0.725628 | 0.714812 | 0.757962 | 0.735836 | 0.711136 | 00:00:01.374676 |
| 15 | Random Forest: Top 15 Raw from PC | 20.0 | auto | 24 | 48 | 10 | 0.639866 | 0.721608 | 0.724196 | 0.758967 | 0.743882 | 0.717704 | 00:00:01.377978 |
| 16 | Random Forest: Top 15 Raw from PC | 25.0 | auto | 24 | 48 | 10 | 0.639866 | 0.721608 | 0.724196 | 0.752263 | 0.743882 | 0.716363 | 00:00:01.373901 |
| 17 | Random Forest: Top 15 Raw from PC | 30.0 | auto | 24 | 48 | 10 | 0.639866 | 0.721608 | 0.724196 | 0.752263 | 0.743882 | 0.716363 | 00:00:01.378100 |
| 18 | Random Forest: Top 15 Raw from PC | 17.0 | auto | 24 | 48 | 10 | 0.639866 | 0.723953 | 0.724531 | 0.762320 | 0.743882 | 0.718910 | 00:00:01.372676 |
| 19 | Random Forest: Top 15 Raw from PC | 18.0 | auto | 24 | 48 | 10 | 0.639866 | 0.721943 | 0.725871 | 0.754609 | 0.743882 | 0.717234 | 00:00:01.373852 |
| 20 | Random Forest: Top 15 Raw from PC | 19.0 | auto | 24 | 48 | 10 | 0.639866 | 0.721608 | 0.725871 | 0.750251 | 0.743882 | 0.716296 | 00:00:01.375606 |
| 21 | Random Forest: Top 15 Raw from PC | 21.0 | auto | 24 | 48 | 10 | 0.639866 | 0.721608 | 0.724196 | 0.752263 | 0.743882 | 0.716363 | 00:00:01.376500 |
| 22 | Random Forest: Top 15 Raw from PC | 22.0 | auto | 24 | 48 | 10 | 0.639866 | 0.721608 | 0.724196 | 0.755615 | 0.743882 | 0.717033 | 00:00:01.378351 |
| 23 | Random Forest: Top 15 Raw from PC | 23.0 | auto | 24 | 48 | 10 | 0.639866 | 0.721608 | 0.724196 | 0.752263 | 0.743882 | 0.716363 | 00:00:01.376782 |
| 24 | Random Forest: Top 15 Raw from PC | 17.0 | auto | 24 | 48 | 15 | 0.635846 | 0.724958 | 0.722185 | 0.752598 | 0.728126 | 0.712743 | 00:00:01.411495 |
| 25 | Random Forest: Top 15 Raw from PC | 17.0 | auto | 24 | 48 | 20 | 0.635846 | 0.719933 | 0.720845 | 0.748910 | 0.734831 | 0.712073 | 00:00:01.575927 |
| 26 | Random Forest: Top 15 Raw from PC | 17.0 | auto | 24 | 48 | 30 | 0.649916 | 0.711223 | 0.725536 | 0.757627 | 0.731814 | 0.715223 | 00:00:01.515367 |
| 27 | Random Forest: Top 15 Raw from PC | 17.0 | auto | 24 | 48 | 50 | 0.631826 | 0.707873 | 0.719169 | 0.758297 | 0.731143 | 0.709662 | 00:00:01.639182 |
| ModelVersion | max_depth | max_features | min_samples_leaf | min_samples_split | n_estimators | Iteration 0 | Iteration 1 | Iteration 2 | Iteration 3 | Iteration 4 | MeanAccuracy | RunTime | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | Random Forest: With PCA | NaN | auto | 1 | 2 | 10 | 0.553099 | 0.554439 | 0.695710 | 0.688904 | 0.656051 | 0.629641 | 00:00:02.163639 |
| 1 | Random Forest: With PCA | NaN | auto | 4 | 8 | 10 | 0.630151 | 0.635176 | 0.706434 | 0.706336 | 0.699631 | 0.675546 | 00:00:02.098019 |
| 2 | Random Forest: With PCA | NaN | auto | 6 | 12 | 10 | 0.629146 | 0.616750 | 0.693029 | 0.700972 | 0.699631 | 0.667906 | 00:00:02.167306 |
| 3 | Random Forest: With PCA | NaN | auto | 9 | 18 | 10 | 0.659631 | 0.683417 | 0.706434 | 0.713711 | 0.720416 | 0.696722 | 00:00:01.961299 |
| 4 | Random Forest: With PCA | NaN | auto | 10 | 20 | 10 | 0.639866 | 0.654941 | 0.681971 | 0.716728 | 0.714717 | 0.681645 | 00:00:01.993173 |
| 5 | Random Forest: With PCA | NaN | auto | 12 | 24 | 10 | 0.653601 | 0.663987 | 0.700067 | 0.725109 | 0.716728 | 0.691898 | 00:00:01.979795 |
| 6 | Random Forest: With PCA | NaN | auto | 18 | 36 | 10 | 0.651591 | 0.647906 | 0.705429 | 0.712370 | 0.705665 | 0.684592 | 00:00:01.987158 |
| 7 | Random Forest: With PCA | NaN | 5 | 12 | 24 | 10 | 0.655946 | 0.672697 | 0.697721 | 0.687563 | 0.724774 | 0.687740 | 00:00:02.120624 |
| 8 | Random Forest: With PCA | NaN | 10 | 12 | 24 | 10 | 0.640201 | 0.618425 | 0.704759 | 0.703989 | 0.702313 | 0.673938 | 00:00:02.468264 |
| 9 | Random Forest: With PCA | NaN | 15 | 12 | 24 | 10 | 0.637186 | 0.583250 | 0.695040 | 0.710023 | 0.720416 | 0.669183 | 00:00:02.551261 |
| 10 | Random Forest: With PCA | NaN | 20 | 12 | 24 | 10 | 0.665997 | 0.568174 | 0.708445 | 0.707006 | 0.713041 | 0.672533 | 00:00:02.981156 |
| 11 | Random Forest: With PCA | NaN | None | 12 | 24 | 10 | 0.672362 | 0.573534 | 0.703418 | 0.695944 | 0.710023 | 0.671056 | 00:00:03.077044 |
| 12 | Random Forest: With PCA | 10.0 | auto | 12 | 24 | 10 | 0.647236 | 0.678392 | 0.708445 | 0.732484 | 0.718404 | 0.696992 | 00:00:01.986593 |
| 13 | Random Forest: With PCA | 15.0 | auto | 12 | 24 | 10 | 0.663987 | 0.694807 | 0.690349 | 0.724103 | 0.718740 | 0.698397 | 00:00:01.896010 |
| 14 | Random Forest: With PCA | 20.0 | auto | 12 | 24 | 10 | 0.647236 | 0.643216 | 0.687332 | 0.724774 | 0.716393 | 0.683790 | 00:00:01.912437 |
| 15 | Random Forest: With PCA | 25.0 | auto | 12 | 24 | 10 | 0.653601 | 0.663987 | 0.700402 | 0.725109 | 0.717399 | 0.692100 | 00:00:01.882534 |
| 16 | Random Forest: With PCA | 30.0 | auto | 12 | 24 | 10 | 0.653601 | 0.663987 | 0.700067 | 0.725109 | 0.716728 | 0.691898 | 00:00:01.948501 |
| 17 | Random Forest: With PCA | 17.0 | auto | 12 | 24 | 10 | 0.648576 | 0.607035 | 0.696046 | 0.717399 | 0.717399 | 0.677291 | 00:00:01.913335 |
| 18 | Random Forest: With PCA | 18.0 | auto | 12 | 24 | 10 | 0.648241 | 0.655276 | 0.684651 | 0.731478 | 0.720080 | 0.687946 | 00:00:01.942801 |
| 19 | Random Forest: With PCA | 19.0 | auto | 12 | 24 | 10 | 0.651256 | 0.646901 | 0.690013 | 0.724103 | 0.716728 | 0.685800 | 00:00:02.028116 |
| 20 | Random Forest: With PCA | 21.0 | auto | 12 | 24 | 10 | 0.648911 | 0.658291 | 0.687332 | 0.721421 | 0.718740 | 0.686939 | 00:00:01.826345 |
| 21 | Random Forest: With PCA | 22.0 | auto | 12 | 24 | 10 | 0.649916 | 0.692462 | 0.693365 | 0.727120 | 0.718740 | 0.696321 | 00:00:01.941394 |
| 22 | Random Forest: With PCA | 23.0 | auto | 12 | 24 | 10 | 0.653601 | 0.663317 | 0.696716 | 0.728461 | 0.718404 | 0.692100 | 00:00:01.862701 |
| 23 | Random Forest: With PCA | 15.0 | auto | 12 | 24 | 15 | 0.640871 | 0.700503 | 0.698391 | 0.720416 | 0.725779 | 0.697192 | 00:00:01.990700 |
| 24 | Random Forest: With PCA | 15.0 | auto | 12 | 24 | 20 | 0.639196 | 0.680402 | 0.702078 | 0.726785 | 0.714717 | 0.692636 | 00:00:01.988554 |
| 25 | Random Forest: With PCA | 15.0 | auto | 12 | 24 | 30 | 0.650921 | 0.700168 | 0.701408 | 0.723768 | 0.719075 | 0.699068 | 00:00:02.217548 |
| 26 | Random Forest: With PCA | 15.0 | auto | 12 | 24 | 50 | 0.649916 | 0.708543 | 0.700402 | 0.721086 | 0.725779 | 0.701145 | 00:00:02.630188 |
CPU times: user 5min 59s, sys: 10min 3s, total: 16min 3s Wall time: 2min 18s
display(TopResultsDF)
plot = TopResultsDF[["Iteration 0","Iteration 1","Iteration 2","Iteration 3","Iteration 4"]].transpose().plot.line(title = "Top Results Among Varying Model Feature Inputs",rot=45)
plot.set_xlabel("Iterations")
plot.set_ylabel("Accuracies")
plot.legend(loc='center left', bbox_to_anchor=(1.01, .5))
FinalResultsDF = pd.concat([FinalResultsDF, TopResultsDF.sort_values(['MeanAccuracy'], ascending=False)[TopResultsDF.columns].head(1)]).sort_values(['MeanAccuracy'], ascending=False).reset_index(drop=True)
TopResultsDF = pd.DataFrame(columns= ['ModelVersion', 'Iteration 0', 'Iteration 1', 'Iteration 2', 'Iteration 3', 'Iteration 4', 'MeanAccuracy'])
| ModelVersion | Iteration 0 | Iteration 1 | Iteration 2 | Iteration 3 | Iteration 4 | MeanAccuracy | |
|---|---|---|---|---|---|---|---|
| 0 | Random Forest: Top 15 Raw from PC | 0.639866 | 0.723953 | 0.724531 | 0.762320 | 0.743882 | 0.718910 |
| 1 | Random Forest: All Raw Features | 0.651926 | 0.725628 | 0.718834 | 0.757291 | 0.738183 | 0.718373 |
| 2 | Random Forest: With PCA | 0.649916 | 0.708543 | 0.700402 | 0.721086 | 0.725779 | 0.701145 |
/usr/local/es7/lib/python3.5/site-packages/matplotlib/font_manager.py:1297: UserWarning: findfont: Font family ['sans-serif'] not found. Falling back to DejaVu Sans (prop.get_family(), self.defaultFamily[fontext]))
We have created a function to be re-used for our cross-validation Accuracy Scores. Inputs of PCA components, Model CLF object, original sample data, and a CV containing our test/train splits allow us to easily produce an array of Accuracy Scores for the different permutations of models tested. A XXXXXXTBDXXXXX plot is also displayed depicting a view of the misclassification values for each iteration. Finally, a confusion matrix is displayed for the last test/train iteration for further interpretation on results.
def plot_confusion_matrixBinary(cm, classes,
normalize=False,
title='Confusion matrix',
cmap=plt.cm.Blues):
"""
This function prints and plots the confusion matrix.
Normalization can be applied by setting `normalize=True`.
"""
plt.rcParams['figure.figsize'] = (18, 6)
plt.rcParams.update({'font.size': 16})
plt.rc('xtick', labelsize=18)
plt.rc('ytick', labelsize=18)
plt.imshow(cm, interpolation='nearest', cmap=cmap)
plt.title(title, fontsize = 18)
plt.colorbar()
tick_marks = np.arange(len(classes))
plt.xticks(tick_marks, classes, rotation=45)
plt.yticks(tick_marks, classes)
if normalize:
cm = cm.astype('float') / cm.sum(axis=1)[:, np.newaxis]
print("Normalized confusion matrix")
else:
print('Confusion matrix, without normalization')
print(cm)
thresh = cm.max() / 2.
for i, j in itertools.product(range(cm.shape[0]), range(cm.shape[1])):
plt.text(j, i, round(cm[i, j],2),
horizontalalignment="center",
color="white" if cm[i, j] > thresh else "black")
plt.tight_layout()
plt.ylabel('True label', fontsize = 18)
plt.xlabel('Predicted label', fontsize = 18)
plt.show()
def plot_ROC_curve(X, y, mean_tpr, mean_fpr, cv = cv, ):
plt.rcParams['figure.figsize'] = (12, 6)
lw = 2
plt.plot([0, 1], [0, 1], linestyle='--', lw=lw, color='k',
label='Luck')
mean_tpr /= cv.get_n_splits(X, y)
mean_tpr[-1] = 1.0
mean_auc = auc(mean_fpr, mean_tpr)
plt.plot(mean_fpr, mean_tpr, color='g', linestyle='--',
label='Mean ROC (area = %0.2f)' % mean_auc, lw=lw)
plt.xlim([-0.05, 1.05])
plt.ylim([-0.05, 1.05])
plt.xlabel('False Positive Rate')
plt.ylabel('True Positive Rate')
plt.title('Receiver operating characteristic (ROC) Curve')
plt.legend(loc="lower right")
plt.show()
%%time
def compute_kfold_scores_ClassificationBinary( clf,
PCA = "No",
Data = OPMAnalysisDataNoFamBinary,
cols = PCList,
cv = cv):
y = Data["SEP"].values # get the labels we want
y = np.where(y == 'NS', 0, 1) # NS = 0; SC = 1
X = Data[cols].as_matrix()
# Run classifier with cross-validation and plot ROC curves
# setup pipeline to take PCA, then fit a clf model
if(PCA == "No"):
clf_pipe = Pipeline(
[('minMaxScaler', MinMaxScaler()),
('CLF',clf)]
)
else:
clf_pipe = Pipeline(
[('minMaxScaler', MinMaxScaler()),
('PCA', PCA),
('CLF',clf)]
)
colors = cycle(['cyan', 'indigo', 'seagreen', 'yellow', 'blue', 'darkorange', 'pink', 'darkred', 'dimgray', 'maroon', 'coral'])
mean_tpr = 0.0
mean_fpr = np.linspace(0, 1, 100)
lw = 2
i = 0
accuracy = []
#logloss = []
for (train, test), color in zip(cv.split(X, y), colors):
clf_pipe.fit(X[train],y[train]) # train object
y_hat = clf_pipe.predict(X[test]) # get test set preditions
probas_ = clf_pipe.fit(X[train], y[train]).predict_proba(X[test])
# Compute ROC curve and area the curve
fpr, tpr, thresholds = roc_curve(y[test], probas_[:, 1])
mean_tpr += interp(mean_fpr, fpr, tpr)
mean_tpr[0] = 0.0
roc_auc = auc(fpr, tpr)
plt.rcParams['figure.figsize'] = (12, 6)
plt.plot(fpr, tpr, lw=lw, color=color,
label='ROC fold %d (area = %0.2f)' % (i, roc_auc))
i += 1
plot_ROC_curve(X, y, mean_tpr, mean_fpr)
#logloss.append(round(l,5))
#print("Accuracy Ratings across all iterations: {0}\n\n\
#Average Accuracy: {1}\n\n\
#Log Loss Values across all iterations: {2}\n\n\
#Average Log Loss: {3}\n".format(accuracy, round(sum(accuracy)/len(accuracy),5), logloss,round(sum(logloss)/len(logloss),5)))
for (train, test), color in zip(cv.split(X, y), colors):
clf_pipe.fit(X[train],y[train]) # train object
y_hat = clf_pipe.predict(X[test]) # get test set preditions
a = float(mt.accuracy_score(y[test],y_hat))
#l = float(mt.log_loss(y[test], y_hat))
accuracy.append(round(a,5))
ytestnames = np.where(y[test] == 0,'NS','SC')
yhatnames = np.where(y_hat == 0,'NS', 'SC')
#print(set(list(y_hat)))
print("confusion matrix\n{0}\n".format(pd.crosstab(ytestnames, yhatnames, rownames = ['True'], colnames = ['Predicted'], margins = True)))
# Plot non-normalized confusion matrix
plt.figure()
plot_confusion_matrixBinary(confusion_matrix(y[test], y_hat),
classes =["NS", "SC"],
normalize =True,
title ='Confusion matrix, with normalization')
print("Accuracy Ratings across all iterations: {0}\n\n\
Average Accuracy: {1}\n".format(accuracy, round(sum(accuracy)/len(accuracy),5)))
return clf_pipe.named_steps['CLF'], accuracy
CPU times: user 5 µs, sys: 0 ns, total: 5 µs Wall time: 9.54 µs
%%time
rfc_clf = RandomForestClassifier(n_estimators = 10,
max_features = 'auto',
max_depth = 17.0,
min_samples_split = 48,
min_samples_leaf = 24,
n_jobs = -1,
random_state = seed) # get object
rfc_clf, rfc_acc = compute_kfold_scores_ClassificationBinary(rfc_clf,
##PCA = PCA(n_components=22, svd_solver='randomized', random_state = seed),
cols = PCList)
/usr/local/es7/lib/python3.5/site-packages/matplotlib/font_manager.py:1297: UserWarning: findfont: Font family ['sans-serif'] not found. Falling back to DejaVu Sans (prop.get_family(), self.defaultFamily[fontext]))
confusion matrix Predicted NS SC All True NS 796 704 1500 SC 371 1114 1485 All 1167 1818 2985 Normalized confusion matrix [[ 0.53066667 0.46933333] [ 0.24983165 0.75016835]]
/usr/local/es7/lib/python3.5/site-packages/matplotlib/font_manager.py:1297: UserWarning: findfont: Font family ['sans-serif'] not found. Falling back to DejaVu Sans (prop.get_family(), self.defaultFamily[fontext]))
confusion matrix Predicted NS SC All True NS 1350 150 1500 SC 674 811 1485 All 2024 961 2985 Normalized confusion matrix [[ 0.9 0.1 ] [ 0.45387205 0.54612795]]
/usr/local/es7/lib/python3.5/site-packages/matplotlib/font_manager.py:1297: UserWarning: findfont: Font family ['sans-serif'] not found. Falling back to DejaVu Sans (prop.get_family(), self.defaultFamily[fontext]))
confusion matrix Predicted NS SC All True NS 773 726 1499 SC 96 1389 1485 All 869 2115 2984 Normalized confusion matrix [[ 0.51567712 0.48432288] [ 0.06464646 0.93535354]]
/usr/local/es7/lib/python3.5/site-packages/matplotlib/font_manager.py:1297: UserWarning: findfont: Font family ['sans-serif'] not found. Falling back to DejaVu Sans (prop.get_family(), self.defaultFamily[fontext]))
confusion matrix Predicted NS SC All True NS 944 555 1499 SC 154 1330 1484 All 1098 1885 2983 Normalized confusion matrix [[ 0.62975317 0.37024683] [ 0.10377358 0.89622642]]
/usr/local/es7/lib/python3.5/site-packages/matplotlib/font_manager.py:1297: UserWarning: findfont: Font family ['sans-serif'] not found. Falling back to DejaVu Sans (prop.get_family(), self.defaultFamily[fontext]))
confusion matrix Predicted NS SC All True NS 984 515 1499 SC 249 1235 1484 All 1233 1750 2983 Normalized confusion matrix [[ 0.65643763 0.34356237] [ 0.16778976 0.83221024]]
/usr/local/es7/lib/python3.5/site-packages/matplotlib/font_manager.py:1297: UserWarning: findfont: Font family ['sans-serif'] not found. Falling back to DejaVu Sans (prop.get_family(), self.defaultFamily[fontext]))
Accuracy Ratings across all iterations: [0.63987, 0.72395, 0.72453, 0.76232, 0.74388]
Average Accuracy: 0.71891
CPU times: user 6.44 s, sys: 2.39 s, total: 8.83 s
Wall time: 5.77 s
Algorithm
Options include "Ball Tree" and "KD Tree".
Our findings, were that the Ball Tree algorithm was considerably less efficient to produce results for all 10 iterations in comparison to the KD Tree Algorithm.
Leaf Size
The size for leaf nodes in the KNN Tree.
Number of Neighbors
After 24 iterations of modifying the above parameters, we land on a final winner based on the highest average Accuracy value across all iterations. Average Accuracy values in our 10 test/train iterations ranged from 66.5216 % from the worst parameter inputs of the Ball_Tree Algorith to a value of 69.5528 % in best tuned KNN Classification model fit. We have chosen to utilize the best input for KD tree, although losing an improvement of .0004 % due to the cost(slower runtime of 07 Minutes 25 Seconds through 10 iterations) of fitting the model as Ball Tree. Parameter inputs for the final K Nearest Neighbor Classification model with the KD Tree Algorithm are as follows:
| algorithm | leaf_size | n_neighbors |
|---|---|---|
| kd_tree | 50 | 150 |
%%time
def knn_explorBinary_w_PCA(n_neighbors,
algorithm ,
leaf_size,
PCA,
Data = OPMAnalysisDataNoFamBinary,
cv = cv,
seed = seed):
startTime = datetime.now()
y = Data["SEP"].values # get the labels we want
X = Data.drop("SEP", axis=1).as_matrix()
knn_clf = KNeighborsClassifier(n_neighbors = n_neighbors, algorithm = algorithm, leaf_size = leaf_size, n_jobs=-1) # get object
# setup pipeline to take PCA, then fit a clf model
clf_pipe = Pipeline(
[('minMaxScaler', MinMaxScaler()),
('PCA', PCA),
('CLF',knn_clf)]
)
accuracy = cross_val_score(clf_pipe, X, y, cv=cv.split(X, y)) # this also can help with parallelism
MeanAccuracy = sum(accuracy)/len(accuracy)
accuracy = np.append(accuracy, MeanAccuracy)
endTime = datetime.now()
TotalTime = endTime - startTime
accuracy = np.append(accuracy, TotalTime)
#print(TotalTime)
#print(accuracy)
return accuracy
CPU times: user 13 µs, sys: 7 µs, total: 20 µs Wall time: 11.9 µs
%%time
def knn_explorBinary(n_neighbors,
algorithm ,
leaf_size,
Data = OPMAnalysisDataNoFamBinary,
cols = PCList,
cv = cv,
seed = seed):
startTime = datetime.now()
y = Data["SEP"].values # get the labels we want
if ("SEP" in cols): X = Data[cols].drop("SEP", axis=1).as_matrix()
else: X = Data[cols]
knn_clf = KNeighborsClassifier(n_neighbors = n_neighbors, algorithm = algorithm, leaf_size = leaf_size, n_jobs=-1) # get object
# setup pipeline to take PCA, then fit a clf model
clf_pipe = Pipeline(
[('minMaxScaler', MinMaxScaler()),
('CLF',knn_clf)]
)
accuracy = cross_val_score(clf_pipe, X, y, cv=cv.split(X, y)) # this also can help with parallelism
MeanAccuracy = sum(accuracy)/len(accuracy)
accuracy = np.append(accuracy, MeanAccuracy)
endTime = datetime.now()
TotalTime = endTime - startTime
accuracy = np.append(accuracy, TotalTime)
#print(TotalTime)
#print(accuracy)
return accuracy
CPU times: user 13 µs, sys: 8 µs, total: 21 µs Wall time: 12.2 µs
%%time
###Full Columns
acclist = []
n_neighbors = [5 , 10 , 15 , 20 , 30 , 40 , 50 , 100 , 150 , 200 , 250 , 150 , 150 , 150 , 150 , 150 , 150 ]
algorithm = 'kd_tree'
leaf_size = [30 , 30 , 30 , 30 , 30 , 30 , 30 , 30 , 30 , 30 , 30 , 2 , 3 , 4 , 5 , 10 , 20 ]
for i in range(0,len(n_neighbors)):
acclist.append(knn_explorBinary(n_neighbors = n_neighbors[i],
algorithm = algorithm,
leaf_size = leaf_size[i],
cols = fullColumns
)
)
rfcdf = pd.DataFrame(pd.concat([pd.DataFrame({
"ModelVersion": "KNN: " + algorithm + ", Full Raw Columns",
"n_neighbors": n_neighbors,
"algorithm": algorithm,
"leaf_size": leaf_size
}),
pd.DataFrame(acclist)], axis = 1).reindex())
rfcdf.columns = ['ModelVersion','algorithm', 'leaf_size','n_neighbors', 'Iteration 0', 'Iteration 1', 'Iteration 2', 'Iteration 3', 'Iteration 4', 'MeanAccuracy', 'RunTime']
TopResultsDF = pd.concat([TopResultsDF, rfcdf.sort_values(['MeanAccuracy'], ascending=False)[TopResultsDF.columns].head(1)]).sort_values(['MeanAccuracy'], ascending=False).reset_index(drop=True)
display(rfcdf)
del rfcdf, acclist
acclist = []
n_neighbors = [5 , 10 , 15 , 20 , 30 , 40 , 50 , 100 , 150 , 200 , 250 , 150 , 150 , 150 , 150 , 150 , 150 ]
algorithm = 'ball_tree'
leaf_size = [30 , 30 , 30 , 30 , 30 , 30 , 30 , 30 , 30 , 30 , 30 , 2 , 3 , 4 , 5 , 10 , 20 ]
for i in range(0,len(n_neighbors)):
acclist.append(knn_explorBinary(n_neighbors = n_neighbors[i],
algorithm = algorithm,
leaf_size = leaf_size[i],
cols = fullColumns
)
)
rfcdf = pd.DataFrame(pd.concat([pd.DataFrame({
"ModelVersion": "KNN: " + algorithm + ", Full Raw Columns",
"n_neighbors": n_neighbors,
"algorithm": algorithm,
"leaf_size": leaf_size
}),
pd.DataFrame(acclist)], axis = 1).reindex())
rfcdf.columns = ['ModelVersion','algorithm', 'leaf_size','n_neighbors', 'Iteration 0', 'Iteration 1', 'Iteration 2', 'Iteration 3', 'Iteration 4', 'MeanAccuracy', 'RunTime']
display(rfcdf)
TopResultsDF = pd.concat([TopResultsDF, rfcdf.sort_values(['MeanAccuracy'], ascending=False)[TopResultsDF.columns].head(1)]).sort_values(['MeanAccuracy'], ascending=False).reset_index(drop=True)
del rfcdf, acclist
###Reduced Columns
acclist = []
n_neighbors = [5 , 10 , 15 , 20 , 30 , 40 , 50 , 100 , 150 , 200 , 250 , 300 , 350 , 400 , 50 , 50 , 50 , 50 , 50 , 50 ]
algorithm = 'kd_tree'
leaf_size = [30 , 30 , 30 , 30 , 30 , 30 , 30 , 30 , 30 , 30 , 30 , 30 , 30 , 30 , 2 , 3 , 4 , 5 , 10 , 20 ]
for i in range(0,len(n_neighbors)):
acclist.append(knn_explorBinary(n_neighbors = n_neighbors[i],
algorithm = algorithm,
leaf_size = leaf_size[i]
)
)
rfcdf = pd.DataFrame(pd.concat([pd.DataFrame({
"ModelVersion": "KNN: " + algorithm + ", Reduced Raw Columns",
"n_neighbors": n_neighbors,
"algorithm": algorithm,
"leaf_size": leaf_size
}),
pd.DataFrame(acclist)], axis = 1).reindex())
rfcdf.columns = ['ModelVersion','algorithm', 'leaf_size','n_neighbors', 'Iteration 0', 'Iteration 1', 'Iteration 2', 'Iteration 3', 'Iteration 4', 'MeanAccuracy', 'RunTime']
display(rfcdf)
TopResultsDF = pd.concat([TopResultsDF, rfcdf.sort_values(['MeanAccuracy'], ascending=False)[TopResultsDF.columns].head(1)]).sort_values(['MeanAccuracy'], ascending=False).reset_index(drop=True)
del rfcdf, acclist
acclist = []
n_neighbors = [5 , 10 , 15 , 20 , 30 , 40 , 50 , 100 , 150 , 200 , 250 , 300 , 350 , 400 , 50 , 50 , 50 , 50 , 50 , 50 ]
algorithm = 'ball_tree'
leaf_size = [30 , 30 , 30 , 30 , 30 , 30 , 30 , 30 , 30 , 30 , 30 , 30 , 30 , 30 , 2 , 3 , 4 , 5 , 10 , 20 ]
for i in range(0,len(n_neighbors)):
acclist.append(knn_explorBinary(n_neighbors = n_neighbors[i],
algorithm = algorithm,
leaf_size = leaf_size[i]
)
)
rfcdf = pd.DataFrame(pd.concat([pd.DataFrame({
"ModelVersion": "KNN: " + algorithm + ", Reduced Raw Columns",
"n_neighbors": n_neighbors,
"algorithm": algorithm,
"leaf_size": leaf_size
}),
pd.DataFrame(acclist)], axis = 1).reindex())
rfcdf.columns = ['ModelVersion','algorithm', 'leaf_size','n_neighbors', 'Iteration 0', 'Iteration 1', 'Iteration 2', 'Iteration 3', 'Iteration 4', 'MeanAccuracy', 'RunTime']
display(rfcdf)
TopResultsDF = pd.concat([TopResultsDF, rfcdf.sort_values(['MeanAccuracy'], ascending=False)[TopResultsDF.columns].head(1)]).sort_values(['MeanAccuracy'], ascending=False).reset_index(drop=True)
del rfcdf, acclist
#### WITH PCA
acclist = []
n_neighbors = [5 , 10 , 15 , 20 , 30 , 40 , 50 , 100 , 150 , 200 , 250 , 100 , 100 , 100 , 100 , 100 , 100 ]
algorithm = 'kd_tree'
leaf_size = [30 , 30 , 30 , 30 , 30 , 30 , 30 , 30 , 30 , 30 , 30 , 2 , 3 , 4 , 5 , 10 , 20 ]
for i in range(0,len(n_neighbors)):
acclist.append(knn_explorBinary_w_PCA(n_neighbors = n_neighbors[i],
algorithm = algorithm,
leaf_size = leaf_size[i],
PCA = PCA(n_components=22, svd_solver='randomized', random_state = seed)
)
)
rfcdf = pd.DataFrame(pd.concat([pd.DataFrame({
"ModelVersion": "KNN: " + algorithm + ", With PCA",
"n_neighbors": n_neighbors,
"algorithm": algorithm,
"leaf_size": leaf_size
}),
pd.DataFrame(acclist)], axis = 1).reindex())
rfcdf.columns = ['ModelVersion','algorithm', 'leaf_size','n_neighbors', 'Iteration 0', 'Iteration 1', 'Iteration 2', 'Iteration 3', 'Iteration 4', 'MeanAccuracy', 'RunTime']
display(rfcdf)
TopResultsDF = pd.concat([TopResultsDF, rfcdf.sort_values(['MeanAccuracy'], ascending=False)[TopResultsDF.columns].head(1)]).sort_values(['MeanAccuracy'], ascending=False).reset_index(drop=True)
del rfcdf, acclist
acclist = []
n_neighbors = [5 , 10 , 15 , 20 , 30 , 40 , 50 , 100 , 150 , 200 , 250 , 100 , 100 , 100 , 100 , 100 , 100 ]
algorithm = 'ball_tree'
leaf_size = [30 , 30 , 30 , 30 , 30 , 30 , 30 , 30 , 30 , 30 , 30 , 2 , 3 , 4 , 5 , 10 , 20 ]
for i in range(0,len(n_neighbors)):
acclist.append(knn_explorBinary_w_PCA(n_neighbors = n_neighbors[i],
algorithm = algorithm,
leaf_size = leaf_size[i],
PCA = PCA(n_components=22, svd_solver='randomized', random_state = seed)
)
)
rfcdf = pd.DataFrame(pd.concat([pd.DataFrame({
"ModelVersion": "KNN: " + algorithm + ", With PCA",
"n_neighbors": n_neighbors,
"algorithm": algorithm,
"leaf_size": leaf_size
}),
pd.DataFrame(acclist)], axis = 1).reindex())
rfcdf.columns = ['ModelVersion','algorithm', 'leaf_size','n_neighbors', 'Iteration 0', 'Iteration 1', 'Iteration 2', 'Iteration 3', 'Iteration 4', 'MeanAccuracy', 'RunTime']
display(rfcdf)
TopResultsDF = pd.concat([TopResultsDF, rfcdf.sort_values(['MeanAccuracy'], ascending=False)[TopResultsDF.columns].head(1)]).sort_values(['MeanAccuracy'], ascending=False).reset_index(drop=True)
del rfcdf, acclist
| ModelVersion | algorithm | leaf_size | n_neighbors | Iteration 0 | Iteration 1 | Iteration 2 | Iteration 3 | Iteration 4 | MeanAccuracy | RunTime | |
|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | KNN: kd_tree, Full Raw Columns | kd_tree | 30 | 5 | 0.643216 | 0.754439 | 0.658512 | 0.704325 | 0.681864 | 0.688471 | 00:00:07.215297 |
| 1 | KNN: kd_tree, Full Raw Columns | kd_tree | 30 | 10 | 0.661977 | 0.749079 | 0.673257 | 0.715052 | 0.694938 | 0.698861 | 00:00:10.744008 |
| 2 | KNN: kd_tree, Full Raw Columns | kd_tree | 30 | 15 | 0.672027 | 0.771859 | 0.698391 | 0.729132 | 0.712035 | 0.716689 | 00:00:11.651342 |
| 3 | KNN: kd_tree, Full Raw Columns | kd_tree | 30 | 20 | 0.664992 | 0.766834 | 0.693365 | 0.731143 | 0.707677 | 0.712802 | 00:00:12.260727 |
| 4 | KNN: kd_tree, Full Raw Columns | kd_tree | 30 | 30 | 0.658961 | 0.764489 | 0.697721 | 0.738183 | 0.710694 | 0.714010 | 00:00:12.931918 |
| 5 | KNN: kd_tree, Full Raw Columns | kd_tree | 30 | 40 | 0.672027 | 0.762144 | 0.698056 | 0.739189 | 0.717734 | 0.717830 | 00:00:13.609490 |
| 6 | KNN: kd_tree, Full Raw Columns | kd_tree | 30 | 50 | 0.684087 | 0.768509 | 0.693365 | 0.734495 | 0.718404 | 0.719772 | 00:00:13.709806 |
| 7 | KNN: kd_tree, Full Raw Columns | kd_tree | 30 | 100 | 0.699162 | 0.761474 | 0.699397 | 0.743212 | 0.725444 | 0.725738 | 00:00:14.866468 |
| 8 | KNN: kd_tree, Full Raw Columns | kd_tree | 30 | 150 | 0.695477 | 0.762814 | 0.706099 | 0.741871 | 0.725779 | 0.726408 | 00:00:14.326372 |
| 9 | KNN: kd_tree, Full Raw Columns | kd_tree | 30 | 200 | 0.693467 | 0.757789 | 0.711126 | 0.737513 | 0.726450 | 0.725269 | 00:00:13.172310 |
| 10 | KNN: kd_tree, Full Raw Columns | kd_tree | 30 | 250 | 0.693802 | 0.750754 | 0.710121 | 0.733825 | 0.717734 | 0.721247 | 00:00:14.175708 |
| 11 | KNN: kd_tree, Full Raw Columns | kd_tree | 2 | 150 | 0.695477 | 0.762814 | 0.706099 | 0.741871 | 0.725779 | 0.726408 | 00:00:23.934619 |
| 12 | KNN: kd_tree, Full Raw Columns | kd_tree | 3 | 150 | 0.695477 | 0.762814 | 0.706099 | 0.741871 | 0.725779 | 0.726408 | 00:00:19.013295 |
| 13 | KNN: kd_tree, Full Raw Columns | kd_tree | 4 | 150 | 0.695477 | 0.762814 | 0.706099 | 0.741871 | 0.725779 | 0.726408 | 00:00:18.861335 |
| 14 | KNN: kd_tree, Full Raw Columns | kd_tree | 5 | 150 | 0.695477 | 0.762814 | 0.706099 | 0.741871 | 0.725779 | 0.726408 | 00:00:18.719492 |
| 15 | KNN: kd_tree, Full Raw Columns | kd_tree | 10 | 150 | 0.695477 | 0.762814 | 0.706099 | 0.741871 | 0.725779 | 0.726408 | 00:00:15.494494 |
| 16 | KNN: kd_tree, Full Raw Columns | kd_tree | 20 | 150 | 0.695477 | 0.762814 | 0.706099 | 0.741871 | 0.725779 | 0.726408 | 00:00:11.930266 |
| ModelVersion | algorithm | leaf_size | n_neighbors | Iteration 0 | Iteration 1 | Iteration 2 | Iteration 3 | Iteration 4 | MeanAccuracy | RunTime | |
|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | KNN: ball_tree, Full Raw Columns | ball_tree | 30 | 5 | 0.643551 | 0.754439 | 0.658512 | 0.704325 | 0.681864 | 0.688538 | 00:00:13.684411 |
| 1 | KNN: ball_tree, Full Raw Columns | ball_tree | 30 | 10 | 0.661977 | 0.749079 | 0.673257 | 0.715052 | 0.694938 | 0.698861 | 00:00:14.519908 |
| 2 | KNN: ball_tree, Full Raw Columns | ball_tree | 30 | 15 | 0.672027 | 0.771859 | 0.698391 | 0.729132 | 0.712035 | 0.716689 | 00:00:13.215811 |
| 3 | KNN: ball_tree, Full Raw Columns | ball_tree | 30 | 20 | 0.664992 | 0.766834 | 0.693365 | 0.731143 | 0.707677 | 0.712802 | 00:00:14.310932 |
| 4 | KNN: ball_tree, Full Raw Columns | ball_tree | 30 | 30 | 0.658961 | 0.764489 | 0.697721 | 0.738183 | 0.710694 | 0.714010 | 00:00:14.023532 |
| 5 | KNN: ball_tree, Full Raw Columns | ball_tree | 30 | 40 | 0.672027 | 0.762144 | 0.698056 | 0.739189 | 0.717734 | 0.717830 | 00:00:14.540020 |
| 6 | KNN: ball_tree, Full Raw Columns | ball_tree | 30 | 50 | 0.684087 | 0.768509 | 0.693365 | 0.734495 | 0.718404 | 0.719772 | 00:00:14.449328 |
| 7 | KNN: ball_tree, Full Raw Columns | ball_tree | 30 | 100 | 0.699162 | 0.761474 | 0.699397 | 0.743212 | 0.725444 | 0.725738 | 00:00:14.089183 |
| 8 | KNN: ball_tree, Full Raw Columns | ball_tree | 30 | 150 | 0.695477 | 0.762814 | 0.706099 | 0.741871 | 0.725779 | 0.726408 | 00:00:14.615003 |
| 9 | KNN: ball_tree, Full Raw Columns | ball_tree | 30 | 200 | 0.693467 | 0.757789 | 0.711126 | 0.737513 | 0.726450 | 0.725269 | 00:00:14.206704 |
| 10 | KNN: ball_tree, Full Raw Columns | ball_tree | 30 | 250 | 0.693802 | 0.750754 | 0.710121 | 0.733825 | 0.717734 | 0.721247 | 00:00:11.974511 |
| 11 | KNN: ball_tree, Full Raw Columns | ball_tree | 2 | 150 | 0.695477 | 0.762814 | 0.706099 | 0.741871 | 0.725779 | 0.726408 | 00:00:26.763267 |
| 12 | KNN: ball_tree, Full Raw Columns | ball_tree | 3 | 150 | 0.695477 | 0.762814 | 0.706099 | 0.741871 | 0.725779 | 0.726408 | 00:00:17.567419 |
| 13 | KNN: ball_tree, Full Raw Columns | ball_tree | 4 | 150 | 0.695477 | 0.762814 | 0.706099 | 0.741871 | 0.725779 | 0.726408 | 00:00:16.859864 |
| 14 | KNN: ball_tree, Full Raw Columns | ball_tree | 5 | 150 | 0.695477 | 0.762814 | 0.706099 | 0.741871 | 0.725779 | 0.726408 | 00:00:17.337983 |
| 15 | KNN: ball_tree, Full Raw Columns | ball_tree | 10 | 150 | 0.695477 | 0.762814 | 0.706099 | 0.741871 | 0.725779 | 0.726408 | 00:00:13.729138 |
| 16 | KNN: ball_tree, Full Raw Columns | ball_tree | 20 | 150 | 0.695477 | 0.762814 | 0.706099 | 0.741871 | 0.725779 | 0.726408 | 00:00:13.104679 |
| ModelVersion | algorithm | leaf_size | n_neighbors | Iteration 0 | Iteration 1 | Iteration 2 | Iteration 3 | Iteration 4 | MeanAccuracy | RunTime | |
|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | KNN: kd_tree, Reduced Raw Columns | kd_tree | 30 | 5 | 0.608040 | 0.757789 | 0.656166 | 0.710023 | 0.686222 | 0.683648 | 00:00:04.043538 |
| 1 | KNN: kd_tree, Reduced Raw Columns | kd_tree | 30 | 10 | 0.628141 | 0.735343 | 0.660188 | 0.723098 | 0.700302 | 0.689414 | 00:00:05.459273 |
| 2 | KNN: kd_tree, Reduced Raw Columns | kd_tree | 30 | 15 | 0.661977 | 0.773869 | 0.669906 | 0.738518 | 0.714717 | 0.711797 | 00:00:05.546211 |
| 3 | KNN: kd_tree, Reduced Raw Columns | kd_tree | 30 | 20 | 0.661307 | 0.769179 | 0.682306 | 0.733490 | 0.709353 | 0.711127 | 00:00:06.208111 |
| 4 | KNN: kd_tree, Reduced Raw Columns | kd_tree | 30 | 30 | 0.684087 | 0.762814 | 0.690684 | 0.735166 | 0.713711 | 0.717292 | 00:00:05.828778 |
| 5 | KNN: kd_tree, Reduced Raw Columns | kd_tree | 30 | 40 | 0.684757 | 0.764824 | 0.693365 | 0.733490 | 0.722762 | 0.719840 | 00:00:07.358399 |
| 6 | KNN: kd_tree, Reduced Raw Columns | kd_tree | 30 | 50 | 0.692462 | 0.767504 | 0.702413 | 0.732149 | 0.717399 | 0.722385 | 00:00:06.869878 |
| 7 | KNN: kd_tree, Reduced Raw Columns | kd_tree | 30 | 100 | 0.690117 | 0.761474 | 0.701408 | 0.732819 | 0.718404 | 0.720844 | 00:00:08.407693 |
| 8 | KNN: kd_tree, Reduced Raw Columns | kd_tree | 30 | 150 | 0.694807 | 0.756449 | 0.701408 | 0.730473 | 0.714381 | 0.719504 | 00:00:10.797124 |
| 9 | KNN: kd_tree, Reduced Raw Columns | kd_tree | 30 | 200 | 0.703853 | 0.746064 | 0.707105 | 0.726785 | 0.717734 | 0.720308 | 00:00:10.198409 |
| 10 | KNN: kd_tree, Reduced Raw Columns | kd_tree | 30 | 250 | 0.702848 | 0.744054 | 0.706769 | 0.729802 | 0.714381 | 0.719571 | 00:00:10.237977 |
| 11 | KNN: kd_tree, Reduced Raw Columns | kd_tree | 30 | 300 | 0.705528 | 0.743719 | 0.707775 | 0.728797 | 0.712370 | 0.719638 | 00:00:11.786110 |
| 12 | KNN: kd_tree, Reduced Raw Columns | kd_tree | 30 | 350 | 0.704858 | 0.741039 | 0.711126 | 0.726785 | 0.711364 | 0.719034 | 00:00:14.166096 |
| 13 | KNN: kd_tree, Reduced Raw Columns | kd_tree | 30 | 400 | 0.705528 | 0.738693 | 0.710121 | 0.725779 | 0.711700 | 0.718364 | 00:00:09.814639 |
| 14 | KNN: kd_tree, Reduced Raw Columns | kd_tree | 2 | 50 | 0.692462 | 0.767504 | 0.702413 | 0.732149 | 0.717399 | 0.722385 | 00:00:07.926306 |
| 15 | KNN: kd_tree, Reduced Raw Columns | kd_tree | 3 | 50 | 0.692462 | 0.767504 | 0.702413 | 0.732149 | 0.717399 | 0.722385 | 00:00:07.111943 |
| 16 | KNN: kd_tree, Reduced Raw Columns | kd_tree | 4 | 50 | 0.692462 | 0.767504 | 0.702413 | 0.732149 | 0.717399 | 0.722385 | 00:00:07.536898 |
| 17 | KNN: kd_tree, Reduced Raw Columns | kd_tree | 5 | 50 | 0.692462 | 0.767504 | 0.702413 | 0.732149 | 0.717399 | 0.722385 | 00:00:07.219420 |
| 18 | KNN: kd_tree, Reduced Raw Columns | kd_tree | 10 | 50 | 0.692462 | 0.767504 | 0.702413 | 0.732149 | 0.717399 | 0.722385 | 00:00:06.946262 |
| 19 | KNN: kd_tree, Reduced Raw Columns | kd_tree | 20 | 50 | 0.692462 | 0.767504 | 0.702413 | 0.732149 | 0.717399 | 0.722385 | 00:00:06.840629 |
| ModelVersion | algorithm | leaf_size | n_neighbors | Iteration 0 | Iteration 1 | Iteration 2 | Iteration 3 | Iteration 4 | MeanAccuracy | RunTime | |
|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | KNN: ball_tree, Reduced Raw Columns | ball_tree | 30 | 5 | 0.608040 | 0.757789 | 0.656166 | 0.710023 | 0.686557 | 0.683715 | 00:00:11.013109 |
| 1 | KNN: ball_tree, Reduced Raw Columns | ball_tree | 30 | 10 | 0.628141 | 0.735343 | 0.660188 | 0.723098 | 0.700302 | 0.689414 | 00:00:11.181241 |
| 2 | KNN: ball_tree, Reduced Raw Columns | ball_tree | 30 | 15 | 0.661977 | 0.773869 | 0.669906 | 0.738518 | 0.714717 | 0.711797 | 00:00:10.212360 |
| 3 | KNN: ball_tree, Reduced Raw Columns | ball_tree | 30 | 20 | 0.661307 | 0.769179 | 0.682306 | 0.733490 | 0.709353 | 0.711127 | 00:00:11.537538 |
| 4 | KNN: ball_tree, Reduced Raw Columns | ball_tree | 30 | 30 | 0.684087 | 0.762814 | 0.690684 | 0.735166 | 0.713711 | 0.717292 | 00:00:11.116485 |
| 5 | KNN: ball_tree, Reduced Raw Columns | ball_tree | 30 | 40 | 0.684757 | 0.764824 | 0.693365 | 0.733490 | 0.722762 | 0.719840 | 00:00:12.781110 |
| 6 | KNN: ball_tree, Reduced Raw Columns | ball_tree | 30 | 50 | 0.692462 | 0.767504 | 0.702413 | 0.732149 | 0.717399 | 0.722385 | 00:00:09.268821 |
| 7 | KNN: ball_tree, Reduced Raw Columns | ball_tree | 30 | 100 | 0.690117 | 0.761474 | 0.701408 | 0.732819 | 0.718404 | 0.720844 | 00:00:12.171072 |
| 8 | KNN: ball_tree, Reduced Raw Columns | ball_tree | 30 | 150 | 0.694807 | 0.756449 | 0.701408 | 0.730473 | 0.714381 | 0.719504 | 00:00:11.399676 |
| 9 | KNN: ball_tree, Reduced Raw Columns | ball_tree | 30 | 200 | 0.703853 | 0.746064 | 0.707105 | 0.726785 | 0.717734 | 0.720308 | 00:00:12.190525 |
| 10 | KNN: ball_tree, Reduced Raw Columns | ball_tree | 30 | 250 | 0.702848 | 0.744054 | 0.706769 | 0.729802 | 0.714381 | 0.719571 | 00:00:11.845569 |
| 11 | KNN: ball_tree, Reduced Raw Columns | ball_tree | 30 | 300 | 0.705528 | 0.743719 | 0.707775 | 0.728797 | 0.712370 | 0.719638 | 00:00:10.856975 |
| 12 | KNN: ball_tree, Reduced Raw Columns | ball_tree | 30 | 350 | 0.704858 | 0.741039 | 0.711126 | 0.726785 | 0.711364 | 0.719034 | 00:00:11.937319 |
| 13 | KNN: ball_tree, Reduced Raw Columns | ball_tree | 30 | 400 | 0.705528 | 0.738693 | 0.710121 | 0.725779 | 0.711700 | 0.718364 | 00:00:11.801615 |
| 14 | KNN: ball_tree, Reduced Raw Columns | ball_tree | 2 | 50 | 0.692462 | 0.767504 | 0.702413 | 0.732149 | 0.717399 | 0.722385 | 00:00:22.156495 |
| 15 | KNN: ball_tree, Reduced Raw Columns | ball_tree | 3 | 50 | 0.692462 | 0.767504 | 0.702413 | 0.732149 | 0.717399 | 0.722385 | 00:00:14.615166 |
| 16 | KNN: ball_tree, Reduced Raw Columns | ball_tree | 4 | 50 | 0.692462 | 0.767504 | 0.702413 | 0.732149 | 0.717399 | 0.722385 | 00:00:16.700274 |
| 17 | KNN: ball_tree, Reduced Raw Columns | ball_tree | 5 | 50 | 0.692462 | 0.767504 | 0.702413 | 0.732149 | 0.717399 | 0.722385 | 00:00:16.319497 |
| 18 | KNN: ball_tree, Reduced Raw Columns | ball_tree | 10 | 50 | 0.692462 | 0.767504 | 0.702413 | 0.732149 | 0.717399 | 0.722385 | 00:00:14.733100 |
| 19 | KNN: ball_tree, Reduced Raw Columns | ball_tree | 20 | 50 | 0.692462 | 0.767504 | 0.702413 | 0.732149 | 0.717399 | 0.722385 | 00:00:10.629029 |
| ModelVersion | algorithm | leaf_size | n_neighbors | Iteration 0 | Iteration 1 | Iteration 2 | Iteration 3 | Iteration 4 | MeanAccuracy | RunTime | |
|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | KNN: kd_tree, With PCA | kd_tree | 30 | 5 | 0.592295 | 0.685427 | 0.656836 | 0.683205 | 0.690245 | 0.661602 | 00:00:04.438011 |
| 1 | KNN: kd_tree, With PCA | kd_tree | 30 | 10 | 0.611725 | 0.715243 | 0.657842 | 0.709353 | 0.701643 | 0.679161 | 00:00:04.660612 |
| 2 | KNN: kd_tree, With PCA | kd_tree | 30 | 15 | 0.652261 | 0.758794 | 0.664209 | 0.716393 | 0.719075 | 0.702146 | 00:00:05.175294 |
| 3 | KNN: kd_tree, With PCA | kd_tree | 30 | 20 | 0.664657 | 0.756784 | 0.673592 | 0.728797 | 0.719075 | 0.708581 | 00:00:06.094096 |
| 4 | KNN: kd_tree, With PCA | kd_tree | 30 | 30 | 0.676047 | 0.759129 | 0.687332 | 0.733490 | 0.720751 | 0.715350 | 00:00:05.051518 |
| 5 | KNN: kd_tree, With PCA | kd_tree | 30 | 40 | 0.690452 | 0.766164 | 0.688003 | 0.732149 | 0.722092 | 0.719772 | 00:00:06.239719 |
| 6 | KNN: kd_tree, With PCA | kd_tree | 30 | 50 | 0.690452 | 0.765494 | 0.691354 | 0.733155 | 0.716728 | 0.719437 | 00:00:07.029738 |
| 7 | KNN: kd_tree, With PCA | kd_tree | 30 | 100 | 0.684087 | 0.763819 | 0.708445 | 0.740194 | 0.721086 | 0.723526 | 00:00:08.565650 |
| 8 | KNN: kd_tree, With PCA | kd_tree | 30 | 150 | 0.688107 | 0.754104 | 0.709786 | 0.726785 | 0.715722 | 0.718901 | 00:00:08.327212 |
| 9 | KNN: kd_tree, With PCA | kd_tree | 30 | 200 | 0.698157 | 0.741374 | 0.713807 | 0.725779 | 0.713711 | 0.718566 | 00:00:10.728146 |
| 10 | KNN: kd_tree, With PCA | kd_tree | 30 | 250 | 0.704523 | 0.742044 | 0.713472 | 0.724103 | 0.720080 | 0.720844 | 00:00:09.806387 |
| 11 | KNN: kd_tree, With PCA | kd_tree | 2 | 100 | 0.684087 | 0.763819 | 0.708445 | 0.740194 | 0.721086 | 0.723526 | 00:00:07.749680 |
| 12 | KNN: kd_tree, With PCA | kd_tree | 3 | 100 | 0.684087 | 0.763819 | 0.708445 | 0.740194 | 0.721086 | 0.723526 | 00:00:07.980740 |
| 13 | KNN: kd_tree, With PCA | kd_tree | 4 | 100 | 0.684087 | 0.763819 | 0.708445 | 0.740194 | 0.721086 | 0.723526 | 00:00:07.626471 |
| 14 | KNN: kd_tree, With PCA | kd_tree | 5 | 100 | 0.684087 | 0.763819 | 0.708445 | 0.740194 | 0.721086 | 0.723526 | 00:00:07.151710 |
| 15 | KNN: kd_tree, With PCA | kd_tree | 10 | 100 | 0.684087 | 0.763819 | 0.708445 | 0.740194 | 0.721086 | 0.723526 | 00:00:07.002556 |
| 16 | KNN: kd_tree, With PCA | kd_tree | 20 | 100 | 0.684087 | 0.763819 | 0.708445 | 0.740194 | 0.721086 | 0.723526 | 00:00:08.341493 |
| ModelVersion | algorithm | leaf_size | n_neighbors | Iteration 0 | Iteration 1 | Iteration 2 | Iteration 3 | Iteration 4 | MeanAccuracy | RunTime | |
|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | KNN: ball_tree, With PCA | ball_tree | 30 | 5 | 0.592295 | 0.685427 | 0.656836 | 0.683205 | 0.690245 | 0.661602 | 00:00:07.799603 |
| 1 | KNN: ball_tree, With PCA | ball_tree | 30 | 10 | 0.611725 | 0.715243 | 0.657842 | 0.709353 | 0.701643 | 0.679161 | 00:00:09.062847 |
| 2 | KNN: ball_tree, With PCA | ball_tree | 30 | 15 | 0.652261 | 0.758794 | 0.664209 | 0.716393 | 0.718740 | 0.702079 | 00:00:09.020782 |
| 3 | KNN: ball_tree, With PCA | ball_tree | 30 | 20 | 0.664657 | 0.756784 | 0.673592 | 0.728797 | 0.719075 | 0.708581 | 00:00:09.204136 |
| 4 | KNN: ball_tree, With PCA | ball_tree | 30 | 30 | 0.676047 | 0.759129 | 0.687332 | 0.733490 | 0.720751 | 0.715350 | 00:00:08.383830 |
| 5 | KNN: ball_tree, With PCA | ball_tree | 30 | 40 | 0.690452 | 0.766164 | 0.688003 | 0.732149 | 0.722092 | 0.719772 | 00:00:09.665761 |
| 6 | KNN: ball_tree, With PCA | ball_tree | 30 | 50 | 0.690452 | 0.765494 | 0.691354 | 0.733155 | 0.716728 | 0.719437 | 00:00:09.484480 |
| 7 | KNN: ball_tree, With PCA | ball_tree | 30 | 100 | 0.684087 | 0.763819 | 0.708445 | 0.740194 | 0.721086 | 0.723526 | 00:00:10.565888 |
| 8 | KNN: ball_tree, With PCA | ball_tree | 30 | 150 | 0.688107 | 0.754104 | 0.709786 | 0.726785 | 0.715722 | 0.718901 | 00:00:08.001522 |
| 9 | KNN: ball_tree, With PCA | ball_tree | 30 | 200 | 0.698157 | 0.741374 | 0.713807 | 0.725779 | 0.713711 | 0.718566 | 00:00:10.745375 |
| 10 | KNN: ball_tree, With PCA | ball_tree | 30 | 250 | 0.704523 | 0.742044 | 0.713472 | 0.724103 | 0.720080 | 0.720844 | 00:00:10.337605 |
| 11 | KNN: ball_tree, With PCA | ball_tree | 2 | 100 | 0.684087 | 0.763819 | 0.708445 | 0.740194 | 0.721086 | 0.723526 | 00:00:11.549839 |
| 12 | KNN: ball_tree, With PCA | ball_tree | 3 | 100 | 0.684087 | 0.763819 | 0.708445 | 0.740194 | 0.721086 | 0.723526 | 00:00:09.969372 |
| 13 | KNN: ball_tree, With PCA | ball_tree | 4 | 100 | 0.684087 | 0.763819 | 0.708445 | 0.740194 | 0.721086 | 0.723526 | 00:00:10.760446 |
| 14 | KNN: ball_tree, With PCA | ball_tree | 5 | 100 | 0.684087 | 0.763819 | 0.708445 | 0.740194 | 0.721086 | 0.723526 | 00:00:11.123610 |
| 15 | KNN: ball_tree, With PCA | ball_tree | 10 | 100 | 0.684087 | 0.763819 | 0.708445 | 0.740194 | 0.721086 | 0.723526 | 00:00:09.711946 |
| 16 | KNN: ball_tree, With PCA | ball_tree | 20 | 100 | 0.684087 | 0.763819 | 0.708445 | 0.740194 | 0.721086 | 0.723526 | 00:00:09.959465 |
CPU times: user 13h 13s, sys: 12min 50s, total: 13h 13min 3s Wall time: 20min 7s
display(TopResultsDF)
plot = TopResultsDF[["Iteration 0","Iteration 1","Iteration 2","Iteration 3","Iteration 4"]].transpose().plot.line(title = "Top Results Among Varying Model Feature Inputs",rot=45)
plot.set_xlabel("Iterations")
plot.set_ylabel("Accuracies")
plot.legend(loc='center left', bbox_to_anchor=(1.01, .5))
FinalResultsDF = pd.concat([FinalResultsDF, TopResultsDF.sort_values(['MeanAccuracy'], ascending=False)[TopResultsDF.columns].head(1)]).sort_values(['MeanAccuracy'], ascending=False).reset_index(drop=True)
TopResultsDF = pd.DataFrame(columns= ['ModelVersion', 'Iteration 0', 'Iteration 1', 'Iteration 2', 'Iteration 3', 'Iteration 4', 'MeanAccuracy'])
| ModelVersion | Iteration 0 | Iteration 1 | Iteration 2 | Iteration 3 | Iteration 4 | MeanAccuracy | |
|---|---|---|---|---|---|---|---|
| 0 | KNN: kd_tree, Full Raw Columns | 0.695477 | 0.762814 | 0.706099 | 0.741871 | 0.725779 | 0.726408 |
| 1 | KNN: ball_tree, Full Raw Columns | 0.695477 | 0.762814 | 0.706099 | 0.741871 | 0.725779 | 0.726408 |
| 2 | KNN: kd_tree, With PCA | 0.684087 | 0.763819 | 0.708445 | 0.740194 | 0.721086 | 0.723526 |
| 3 | KNN: ball_tree, With PCA | 0.684087 | 0.763819 | 0.708445 | 0.740194 | 0.721086 | 0.723526 |
| 4 | KNN: kd_tree, Reduced Raw Columns | 0.692462 | 0.767504 | 0.702413 | 0.732149 | 0.717399 | 0.722385 |
| 5 | KNN: ball_tree, Reduced Raw Columns | 0.692462 | 0.767504 | 0.702413 | 0.732149 | 0.717399 | 0.722385 |
/usr/local/es7/lib/python3.5/site-packages/matplotlib/font_manager.py:1297: UserWarning: findfont: Font family ['sans-serif'] not found. Falling back to DejaVu Sans (prop.get_family(), self.defaultFamily[fontext]))
%%time
knn_clf = KNeighborsClassifier(n_neighbors = 150, algorithm = 'ball_tree',leaf_size = 30, n_jobs=-1) # get object
knn_clf, knn_acc = compute_kfold_scores_ClassificationBinary(clf = knn_clf,
cols = fullColumns)
/usr/local/es7/lib/python3.5/site-packages/matplotlib/font_manager.py:1297: UserWarning: findfont: Font family ['sans-serif'] not found. Falling back to DejaVu Sans (prop.get_family(), self.defaultFamily[fontext]))
confusion matrix Predicted NS SC All True NS 1022 478 1500 SC 431 1054 1485 All 1453 1532 2985 Normalized confusion matrix [[ 0.68133333 0.31866667] [ 0.29023569 0.70976431]]
/usr/local/es7/lib/python3.5/site-packages/matplotlib/font_manager.py:1297: UserWarning: findfont: Font family ['sans-serif'] not found. Falling back to DejaVu Sans (prop.get_family(), self.defaultFamily[fontext]))
confusion matrix Predicted NS SC All True NS 1230 270 1500 SC 438 1047 1485 All 1668 1317 2985 Normalized confusion matrix [[ 0.82 0.18 ] [ 0.29494949 0.70505051]]
/usr/local/es7/lib/python3.5/site-packages/matplotlib/font_manager.py:1297: UserWarning: findfont: Font family ['sans-serif'] not found. Falling back to DejaVu Sans (prop.get_family(), self.defaultFamily[fontext]))
confusion matrix Predicted NS SC All True NS 835 664 1499 SC 213 1272 1485 All 1048 1936 2984 Normalized confusion matrix [[ 0.55703803 0.44296197] [ 0.14343434 0.85656566]]
/usr/local/es7/lib/python3.5/site-packages/matplotlib/font_manager.py:1297: UserWarning: findfont: Font family ['sans-serif'] not found. Falling back to DejaVu Sans (prop.get_family(), self.defaultFamily[fontext]))
confusion matrix Predicted NS SC All True NS 1055 444 1499 SC 326 1158 1484 All 1381 1602 2983 Normalized confusion matrix [[ 0.70380254 0.29619746] [ 0.21967655 0.78032345]]
/usr/local/es7/lib/python3.5/site-packages/matplotlib/font_manager.py:1297: UserWarning: findfont: Font family ['sans-serif'] not found. Falling back to DejaVu Sans (prop.get_family(), self.defaultFamily[fontext]))
confusion matrix Predicted NS SC All True NS 1072 427 1499 SC 391 1093 1484 All 1463 1520 2983 Normalized confusion matrix [[ 0.71514343 0.28485657] [ 0.26347709 0.73652291]]
/usr/local/es7/lib/python3.5/site-packages/matplotlib/font_manager.py:1297: UserWarning: findfont: Font family ['sans-serif'] not found. Falling back to DejaVu Sans (prop.get_family(), self.defaultFamily[fontext]))
Accuracy Ratings across all iterations: [0.69548, 0.76281, 0.7061, 0.74187, 0.72578]
Average Accuracy: 0.72641
CPU times: user 23min 17s, sys: 3.27 s, total: 23min 20s
Wall time: 36.1 s
We have chosen to manipulate the cost variable (C) within our logistic regression analyzing accuracies at {1.0, .01, .05, 5}. This parameter is essentially an inverted regularization strength equal to 1/lambda per scikit-learn class function code (lambda being the actual regularization item). Therefore, the smaller the cost value the stronger the regularization.
mapping = {'NS':0, 'SC':1}
y = OPMAnalysisDataNoFamBinary.replace({'SEP': mapping})
y = y.SEP
%%R -i OPMAnalysisDataNoFamBinary,fullColumns,y
install.packages("car") ## Selection 55
require(car)
str(OPMAnalysisDataNoFamBinary)
print(unlist(fullColumns))
print(paste("# of SEP observations = ", length(y)))
print(paste("SEP type = ", unique(y)))
print(paste("SEP class = ", class(y)))
/users3/cse/amfrye/.local/lib/python3.5/site-packages/rpy2/robjects/pandas2ri.py:61: UserWarning: Error while trying to convert the column "AGELVL_B". Fall back to string conversion. The error is: Cannot convert numpy array of unsigned values -- R does not have unsigned integers. (name, str(e))) /users3/cse/amfrye/.local/lib/python3.5/site-packages/rpy2/robjects/pandas2ri.py:61: UserWarning: Error while trying to convert the column "AGELVL_C". Fall back to string conversion. The error is: Cannot convert numpy array of unsigned values -- R does not have unsigned integers. (name, str(e))) /users3/cse/amfrye/.local/lib/python3.5/site-packages/rpy2/robjects/pandas2ri.py:61: UserWarning: Error while trying to convert the column "AGELVL_D". Fall back to string conversion. The error is: Cannot convert numpy array of unsigned values -- R does not have unsigned integers. (name, str(e))) /users3/cse/amfrye/.local/lib/python3.5/site-packages/rpy2/robjects/pandas2ri.py:61: UserWarning: Error while trying to convert the column "AGELVL_E". Fall back to string conversion. The error is: Cannot convert numpy array of unsigned values -- R does not have unsigned integers. (name, str(e))) /users3/cse/amfrye/.local/lib/python3.5/site-packages/rpy2/robjects/pandas2ri.py:61: UserWarning: Error while trying to convert the column "AGELVL_F". Fall back to string conversion. The error is: Cannot convert numpy array of unsigned values -- R does not have unsigned integers. (name, str(e))) /users3/cse/amfrye/.local/lib/python3.5/site-packages/rpy2/robjects/pandas2ri.py:61: UserWarning: Error while trying to convert the column "AGELVL_G". Fall back to string conversion. The error is: Cannot convert numpy array of unsigned values -- R does not have unsigned integers. (name, str(e))) /users3/cse/amfrye/.local/lib/python3.5/site-packages/rpy2/robjects/pandas2ri.py:61: UserWarning: Error while trying to convert the column "AGELVL_H". Fall back to string conversion. The error is: Cannot convert numpy array of unsigned values -- R does not have unsigned integers. (name, str(e))) /users3/cse/amfrye/.local/lib/python3.5/site-packages/rpy2/robjects/pandas2ri.py:61: UserWarning: Error while trying to convert the column "AGELVL_I". Fall back to string conversion. The error is: Cannot convert numpy array of unsigned values -- R does not have unsigned integers. (name, str(e))) /users3/cse/amfrye/.local/lib/python3.5/site-packages/rpy2/robjects/pandas2ri.py:61: UserWarning: Error while trying to convert the column "AGELVL_J". Fall back to string conversion. The error is: Cannot convert numpy array of unsigned values -- R does not have unsigned integers. (name, str(e))) /users3/cse/amfrye/.local/lib/python3.5/site-packages/rpy2/robjects/pandas2ri.py:61: UserWarning: Error while trying to convert the column "AGELVL_K". Fall back to string conversion. The error is: Cannot convert numpy array of unsigned values -- R does not have unsigned integers. (name, str(e))) /users3/cse/amfrye/.local/lib/python3.5/site-packages/rpy2/robjects/pandas2ri.py:61: UserWarning: Error while trying to convert the column "LOC_01". Fall back to string conversion. The error is: Cannot convert numpy array of unsigned values -- R does not have unsigned integers. (name, str(e))) /users3/cse/amfrye/.local/lib/python3.5/site-packages/rpy2/robjects/pandas2ri.py:61: UserWarning: Error while trying to convert the column "LOC_02". Fall back to string conversion. The error is: Cannot convert numpy array of unsigned values -- R does not have unsigned integers. (name, str(e))) /users3/cse/amfrye/.local/lib/python3.5/site-packages/rpy2/robjects/pandas2ri.py:61: UserWarning: Error while trying to convert the column "LOC_04". Fall back to string conversion. The error is: Cannot convert numpy array of unsigned values -- R does not have unsigned integers. (name, str(e))) /users3/cse/amfrye/.local/lib/python3.5/site-packages/rpy2/robjects/pandas2ri.py:61: UserWarning: Error while trying to convert the column "LOC_05". Fall back to string conversion. The error is: Cannot convert numpy array of unsigned values -- R does not have unsigned integers. (name, str(e))) /users3/cse/amfrye/.local/lib/python3.5/site-packages/rpy2/robjects/pandas2ri.py:61: UserWarning: Error while trying to convert the column "LOC_06". Fall back to string conversion. The error is: Cannot convert numpy array of unsigned values -- R does not have unsigned integers. (name, str(e))) /users3/cse/amfrye/.local/lib/python3.5/site-packages/rpy2/robjects/pandas2ri.py:61: UserWarning: Error while trying to convert the column "LOC_08". Fall back to string conversion. The error is: Cannot convert numpy array of unsigned values -- R does not have unsigned integers. (name, str(e))) /users3/cse/amfrye/.local/lib/python3.5/site-packages/rpy2/robjects/pandas2ri.py:61: UserWarning: Error while trying to convert the column "LOC_09". Fall back to string conversion. The error is: Cannot convert numpy array of unsigned values -- R does not have unsigned integers. (name, str(e))) /users3/cse/amfrye/.local/lib/python3.5/site-packages/rpy2/robjects/pandas2ri.py:61: UserWarning: Error while trying to convert the column "LOC_10". Fall back to string conversion. The error is: Cannot convert numpy array of unsigned values -- R does not have unsigned integers. (name, str(e))) /users3/cse/amfrye/.local/lib/python3.5/site-packages/rpy2/robjects/pandas2ri.py:61: UserWarning: Error while trying to convert the column "LOC_11". Fall back to string conversion. The error is: Cannot convert numpy array of unsigned values -- R does not have unsigned integers. (name, str(e))) /users3/cse/amfrye/.local/lib/python3.5/site-packages/rpy2/robjects/pandas2ri.py:61: UserWarning: Error while trying to convert the column "LOC_12". Fall back to string conversion. The error is: Cannot convert numpy array of unsigned values -- R does not have unsigned integers. (name, str(e))) /users3/cse/amfrye/.local/lib/python3.5/site-packages/rpy2/robjects/pandas2ri.py:61: UserWarning: Error while trying to convert the column "LOC_13". Fall back to string conversion. The error is: Cannot convert numpy array of unsigned values -- R does not have unsigned integers. (name, str(e))) /users3/cse/amfrye/.local/lib/python3.5/site-packages/rpy2/robjects/pandas2ri.py:61: UserWarning: Error while trying to convert the column "LOC_15". Fall back to string conversion. The error is: Cannot convert numpy array of unsigned values -- R does not have unsigned integers. (name, str(e))) /users3/cse/amfrye/.local/lib/python3.5/site-packages/rpy2/robjects/pandas2ri.py:61: UserWarning: Error while trying to convert the column "LOC_16". Fall back to string conversion. The error is: Cannot convert numpy array of unsigned values -- R does not have unsigned integers. (name, str(e))) /users3/cse/amfrye/.local/lib/python3.5/site-packages/rpy2/robjects/pandas2ri.py:61: UserWarning: Error while trying to convert the column "LOC_17". Fall back to string conversion. The error is: Cannot convert numpy array of unsigned values -- R does not have unsigned integers. (name, str(e))) /users3/cse/amfrye/.local/lib/python3.5/site-packages/rpy2/robjects/pandas2ri.py:61: UserWarning: Error while trying to convert the column "LOC_18". Fall back to string conversion. The error is: Cannot convert numpy array of unsigned values -- R does not have unsigned integers. (name, str(e))) /users3/cse/amfrye/.local/lib/python3.5/site-packages/rpy2/robjects/pandas2ri.py:61: UserWarning: Error while trying to convert the column "LOC_19". Fall back to string conversion. The error is: Cannot convert numpy array of unsigned values -- R does not have unsigned integers. (name, str(e))) /users3/cse/amfrye/.local/lib/python3.5/site-packages/rpy2/robjects/pandas2ri.py:61: UserWarning: Error while trying to convert the column "LOC_20". Fall back to string conversion. The error is: Cannot convert numpy array of unsigned values -- R does not have unsigned integers. (name, str(e))) /users3/cse/amfrye/.local/lib/python3.5/site-packages/rpy2/robjects/pandas2ri.py:61: UserWarning: Error while trying to convert the column "LOC_21". Fall back to string conversion. The error is: Cannot convert numpy array of unsigned values -- R does not have unsigned integers. (name, str(e))) /users3/cse/amfrye/.local/lib/python3.5/site-packages/rpy2/robjects/pandas2ri.py:61: UserWarning: Error while trying to convert the column "LOC_22". Fall back to string conversion. The error is: Cannot convert numpy array of unsigned values -- R does not have unsigned integers. (name, str(e))) /users3/cse/amfrye/.local/lib/python3.5/site-packages/rpy2/robjects/pandas2ri.py:61: UserWarning: Error while trying to convert the column "LOC_23". Fall back to string conversion. The error is: Cannot convert numpy array of unsigned values -- R does not have unsigned integers. (name, str(e))) /users3/cse/amfrye/.local/lib/python3.5/site-packages/rpy2/robjects/pandas2ri.py:61: UserWarning: Error while trying to convert the column "LOC_24". Fall back to string conversion. The error is: Cannot convert numpy array of unsigned values -- R does not have unsigned integers. (name, str(e))) /users3/cse/amfrye/.local/lib/python3.5/site-packages/rpy2/robjects/pandas2ri.py:61: UserWarning: Error while trying to convert the column "LOC_25". Fall back to string conversion. The error is: Cannot convert numpy array of unsigned values -- R does not have unsigned integers. (name, str(e))) /users3/cse/amfrye/.local/lib/python3.5/site-packages/rpy2/robjects/pandas2ri.py:61: UserWarning: Error while trying to convert the column "LOC_26". Fall back to string conversion. The error is: Cannot convert numpy array of unsigned values -- R does not have unsigned integers. (name, str(e))) /users3/cse/amfrye/.local/lib/python3.5/site-packages/rpy2/robjects/pandas2ri.py:61: UserWarning: Error while trying to convert the column "LOC_27". Fall back to string conversion. The error is: Cannot convert numpy array of unsigned values -- R does not have unsigned integers. (name, str(e))) /users3/cse/amfrye/.local/lib/python3.5/site-packages/rpy2/robjects/pandas2ri.py:61: UserWarning: Error while trying to convert the column "LOC_28". Fall back to string conversion. The error is: Cannot convert numpy array of unsigned values -- R does not have unsigned integers. (name, str(e))) /users3/cse/amfrye/.local/lib/python3.5/site-packages/rpy2/robjects/pandas2ri.py:61: UserWarning: Error while trying to convert the column "LOC_29". Fall back to string conversion. The error is: Cannot convert numpy array of unsigned values -- R does not have unsigned integers. (name, str(e))) /users3/cse/amfrye/.local/lib/python3.5/site-packages/rpy2/robjects/pandas2ri.py:61: UserWarning: Error while trying to convert the column "LOC_30". Fall back to string conversion. The error is: Cannot convert numpy array of unsigned values -- R does not have unsigned integers. (name, str(e))) /users3/cse/amfrye/.local/lib/python3.5/site-packages/rpy2/robjects/pandas2ri.py:61: UserWarning: Error while trying to convert the column "LOC_31". Fall back to string conversion. The error is: Cannot convert numpy array of unsigned values -- R does not have unsigned integers. (name, str(e))) /users3/cse/amfrye/.local/lib/python3.5/site-packages/rpy2/robjects/pandas2ri.py:61: UserWarning: Error while trying to convert the column "LOC_32". Fall back to string conversion. The error is: Cannot convert numpy array of unsigned values -- R does not have unsigned integers. (name, str(e))) /users3/cse/amfrye/.local/lib/python3.5/site-packages/rpy2/robjects/pandas2ri.py:61: UserWarning: Error while trying to convert the column "LOC_33". Fall back to string conversion. The error is: Cannot convert numpy array of unsigned values -- R does not have unsigned integers. (name, str(e))) /users3/cse/amfrye/.local/lib/python3.5/site-packages/rpy2/robjects/pandas2ri.py:61: UserWarning: Error while trying to convert the column "LOC_34". Fall back to string conversion. The error is: Cannot convert numpy array of unsigned values -- R does not have unsigned integers. (name, str(e))) /users3/cse/amfrye/.local/lib/python3.5/site-packages/rpy2/robjects/pandas2ri.py:61: UserWarning: Error while trying to convert the column "LOC_35". Fall back to string conversion. The error is: Cannot convert numpy array of unsigned values -- R does not have unsigned integers. (name, str(e))) /users3/cse/amfrye/.local/lib/python3.5/site-packages/rpy2/robjects/pandas2ri.py:61: UserWarning: Error while trying to convert the column "LOC_36". Fall back to string conversion. The error is: Cannot convert numpy array of unsigned values -- R does not have unsigned integers. (name, str(e))) /users3/cse/amfrye/.local/lib/python3.5/site-packages/rpy2/robjects/pandas2ri.py:61: UserWarning: Error while trying to convert the column "LOC_37". Fall back to string conversion. The error is: Cannot convert numpy array of unsigned values -- R does not have unsigned integers. (name, str(e))) /users3/cse/amfrye/.local/lib/python3.5/site-packages/rpy2/robjects/pandas2ri.py:61: UserWarning: Error while trying to convert the column "LOC_38". Fall back to string conversion. The error is: Cannot convert numpy array of unsigned values -- R does not have unsigned integers. (name, str(e))) /users3/cse/amfrye/.local/lib/python3.5/site-packages/rpy2/robjects/pandas2ri.py:61: UserWarning: Error while trying to convert the column "LOC_39". Fall back to string conversion. The error is: Cannot convert numpy array of unsigned values -- R does not have unsigned integers. (name, str(e))) /users3/cse/amfrye/.local/lib/python3.5/site-packages/rpy2/robjects/pandas2ri.py:61: UserWarning: Error while trying to convert the column "LOC_40". Fall back to string conversion. The error is: Cannot convert numpy array of unsigned values -- R does not have unsigned integers. (name, str(e))) /users3/cse/amfrye/.local/lib/python3.5/site-packages/rpy2/robjects/pandas2ri.py:61: UserWarning: Error while trying to convert the column "LOC_41". Fall back to string conversion. The error is: Cannot convert numpy array of unsigned values -- R does not have unsigned integers. (name, str(e))) /users3/cse/amfrye/.local/lib/python3.5/site-packages/rpy2/robjects/pandas2ri.py:61: UserWarning: Error while trying to convert the column "LOC_42". Fall back to string conversion. The error is: Cannot convert numpy array of unsigned values -- R does not have unsigned integers. (name, str(e))) /users3/cse/amfrye/.local/lib/python3.5/site-packages/rpy2/robjects/pandas2ri.py:61: UserWarning: Error while trying to convert the column "LOC_44". Fall back to string conversion. The error is: Cannot convert numpy array of unsigned values -- R does not have unsigned integers. (name, str(e))) /users3/cse/amfrye/.local/lib/python3.5/site-packages/rpy2/robjects/pandas2ri.py:61: UserWarning: Error while trying to convert the column "LOC_45". Fall back to string conversion. The error is: Cannot convert numpy array of unsigned values -- R does not have unsigned integers. (name, str(e))) /users3/cse/amfrye/.local/lib/python3.5/site-packages/rpy2/robjects/pandas2ri.py:61: UserWarning: Error while trying to convert the column "LOC_46". Fall back to string conversion. The error is: Cannot convert numpy array of unsigned values -- R does not have unsigned integers. (name, str(e))) /users3/cse/amfrye/.local/lib/python3.5/site-packages/rpy2/robjects/pandas2ri.py:61: UserWarning: Error while trying to convert the column "LOC_47". Fall back to string conversion. The error is: Cannot convert numpy array of unsigned values -- R does not have unsigned integers. (name, str(e))) /users3/cse/amfrye/.local/lib/python3.5/site-packages/rpy2/robjects/pandas2ri.py:61: UserWarning: Error while trying to convert the column "LOC_48". Fall back to string conversion. The error is: Cannot convert numpy array of unsigned values -- R does not have unsigned integers. (name, str(e))) /users3/cse/amfrye/.local/lib/python3.5/site-packages/rpy2/robjects/pandas2ri.py:61: UserWarning: Error while trying to convert the column "LOC_49". Fall back to string conversion. The error is: Cannot convert numpy array of unsigned values -- R does not have unsigned integers. (name, str(e))) /users3/cse/amfrye/.local/lib/python3.5/site-packages/rpy2/robjects/pandas2ri.py:61: UserWarning: Error while trying to convert the column "LOC_50". Fall back to string conversion. The error is: Cannot convert numpy array of unsigned values -- R does not have unsigned integers. (name, str(e))) /users3/cse/amfrye/.local/lib/python3.5/site-packages/rpy2/robjects/pandas2ri.py:61: UserWarning: Error while trying to convert the column "LOC_51". Fall back to string conversion. The error is: Cannot convert numpy array of unsigned values -- R does not have unsigned integers. (name, str(e))) /users3/cse/amfrye/.local/lib/python3.5/site-packages/rpy2/robjects/pandas2ri.py:61: UserWarning: Error while trying to convert the column "LOC_53". Fall back to string conversion. The error is: Cannot convert numpy array of unsigned values -- R does not have unsigned integers. (name, str(e))) /users3/cse/amfrye/.local/lib/python3.5/site-packages/rpy2/robjects/pandas2ri.py:61: UserWarning: Error while trying to convert the column "LOC_54". Fall back to string conversion. The error is: Cannot convert numpy array of unsigned values -- R does not have unsigned integers. (name, str(e))) /users3/cse/amfrye/.local/lib/python3.5/site-packages/rpy2/robjects/pandas2ri.py:61: UserWarning: Error while trying to convert the column "LOC_55". Fall back to string conversion. The error is: Cannot convert numpy array of unsigned values -- R does not have unsigned integers. (name, str(e))) /users3/cse/amfrye/.local/lib/python3.5/site-packages/rpy2/robjects/pandas2ri.py:61: UserWarning: Error while trying to convert the column "LOC_56". Fall back to string conversion. The error is: Cannot convert numpy array of unsigned values -- R does not have unsigned integers. (name, str(e))) /users3/cse/amfrye/.local/lib/python3.5/site-packages/rpy2/robjects/pandas2ri.py:61: UserWarning: Error while trying to convert the column "TOA_10". Fall back to string conversion. The error is: Cannot convert numpy array of unsigned values -- R does not have unsigned integers. (name, str(e))) /users3/cse/amfrye/.local/lib/python3.5/site-packages/rpy2/robjects/pandas2ri.py:61: UserWarning: Error while trying to convert the column "TOA_15". Fall back to string conversion. The error is: Cannot convert numpy array of unsigned values -- R does not have unsigned integers. (name, str(e))) /users3/cse/amfrye/.local/lib/python3.5/site-packages/rpy2/robjects/pandas2ri.py:61: UserWarning: Error while trying to convert the column "TOA_20". Fall back to string conversion. The error is: Cannot convert numpy array of unsigned values -- R does not have unsigned integers. (name, str(e))) /users3/cse/amfrye/.local/lib/python3.5/site-packages/rpy2/robjects/pandas2ri.py:61: UserWarning: Error while trying to convert the column "TOA_30". Fall back to string conversion. The error is: Cannot convert numpy array of unsigned values -- R does not have unsigned integers. (name, str(e))) /users3/cse/amfrye/.local/lib/python3.5/site-packages/rpy2/robjects/pandas2ri.py:61: UserWarning: Error while trying to convert the column "TOA_32". Fall back to string conversion. The error is: Cannot convert numpy array of unsigned values -- R does not have unsigned integers. (name, str(e))) /users3/cse/amfrye/.local/lib/python3.5/site-packages/rpy2/robjects/pandas2ri.py:61: UserWarning: Error while trying to convert the column "TOA_35". Fall back to string conversion. The error is: Cannot convert numpy array of unsigned values -- R does not have unsigned integers. (name, str(e))) /users3/cse/amfrye/.local/lib/python3.5/site-packages/rpy2/robjects/pandas2ri.py:61: UserWarning: Error while trying to convert the column "TOA_38". Fall back to string conversion. The error is: Cannot convert numpy array of unsigned values -- R does not have unsigned integers. (name, str(e))) /users3/cse/amfrye/.local/lib/python3.5/site-packages/rpy2/robjects/pandas2ri.py:61: UserWarning: Error while trying to convert the column "TOA_40". Fall back to string conversion. The error is: Cannot convert numpy array of unsigned values -- R does not have unsigned integers. (name, str(e))) /users3/cse/amfrye/.local/lib/python3.5/site-packages/rpy2/robjects/pandas2ri.py:61: UserWarning: Error while trying to convert the column "TOA_42". Fall back to string conversion. The error is: Cannot convert numpy array of unsigned values -- R does not have unsigned integers. (name, str(e))) /users3/cse/amfrye/.local/lib/python3.5/site-packages/rpy2/robjects/pandas2ri.py:61: UserWarning: Error while trying to convert the column "TOA_44". Fall back to string conversion. The error is: Cannot convert numpy array of unsigned values -- R does not have unsigned integers. (name, str(e))) /users3/cse/amfrye/.local/lib/python3.5/site-packages/rpy2/robjects/pandas2ri.py:61: UserWarning: Error while trying to convert the column "TOA_45". Fall back to string conversion. The error is: Cannot convert numpy array of unsigned values -- R does not have unsigned integers. (name, str(e))) /users3/cse/amfrye/.local/lib/python3.5/site-packages/rpy2/robjects/pandas2ri.py:61: UserWarning: Error while trying to convert the column "TOA_48". Fall back to string conversion. The error is: Cannot convert numpy array of unsigned values -- R does not have unsigned integers. (name, str(e))) /users3/cse/amfrye/.local/lib/python3.5/site-packages/rpy2/robjects/pandas2ri.py:61: UserWarning: Error while trying to convert the column "LOCTYP_1". Fall back to string conversion. The error is: Cannot convert numpy array of unsigned values -- R does not have unsigned integers. (name, str(e))) /users3/cse/amfrye/.local/lib/python3.5/site-packages/rpy2/robjects/pandas2ri.py:61: UserWarning: Error while trying to convert the column "PPTYP_1". Fall back to string conversion. The error is: Cannot convert numpy array of unsigned values -- R does not have unsigned integers. (name, str(e))) /users3/cse/amfrye/.local/lib/python3.5/site-packages/rpy2/robjects/pandas2ri.py:61: UserWarning: Error while trying to convert the column "PPGROUP_11". Fall back to string conversion. The error is: Cannot convert numpy array of unsigned values -- R does not have unsigned integers. (name, str(e))) /users3/cse/amfrye/.local/lib/python3.5/site-packages/rpy2/robjects/pandas2ri.py:61: UserWarning: Error while trying to convert the column "PPGROUP_12". Fall back to string conversion. The error is: Cannot convert numpy array of unsigned values -- R does not have unsigned integers. (name, str(e))) /users3/cse/amfrye/.local/lib/python3.5/site-packages/rpy2/robjects/pandas2ri.py:61: UserWarning: Error while trying to convert the column "TOATYP_1". Fall back to string conversion. The error is: Cannot convert numpy array of unsigned values -- R does not have unsigned integers. (name, str(e))) /users3/cse/amfrye/.local/lib/python3.5/site-packages/rpy2/robjects/pandas2ri.py:61: UserWarning: Error while trying to convert the column "TOATYP_2". Fall back to string conversion. The error is: Cannot convert numpy array of unsigned values -- R does not have unsigned integers. (name, str(e))) /users3/cse/amfrye/.local/lib/python3.5/site-packages/rpy2/rinterface/__init__.py:145: RRuntimeWarning: Installing package into ‘/scratch/cse/amfrye/R/x86_64-redhat-linux-gnu-library/3.4’ (as ‘lib’ is unspecified) warnings.warn(x, RRuntimeWarning)
Selection: 55
/users3/cse/amfrye/.local/lib/python3.5/site-packages/rpy2/rinterface/__init__.py:145: RRuntimeWarning: trying URL 'https://mirrors.nics.utk.edu/cran/src/contrib/car_2.1-5.tar.gz' warnings.warn(x, RRuntimeWarning) /users3/cse/amfrye/.local/lib/python3.5/site-packages/rpy2/rinterface/__init__.py:145: RRuntimeWarning: Content type 'application/x-gzip' warnings.warn(x, RRuntimeWarning) /users3/cse/amfrye/.local/lib/python3.5/site-packages/rpy2/rinterface/__init__.py:145: RRuntimeWarning: length 628590 bytes (613 KB) warnings.warn(x, RRuntimeWarning) /users3/cse/amfrye/.local/lib/python3.5/site-packages/rpy2/rinterface/__init__.py:145: RRuntimeWarning: = warnings.warn(x, RRuntimeWarning) /users3/cse/amfrye/.local/lib/python3.5/site-packages/rpy2/rinterface/__init__.py:145: RRuntimeWarning: warnings.warn(x, RRuntimeWarning) /users3/cse/amfrye/.local/lib/python3.5/site-packages/rpy2/rinterface/__init__.py:145: RRuntimeWarning: downloaded 613 KB warnings.warn(x, RRuntimeWarning) /users3/cse/amfrye/.local/lib/python3.5/site-packages/rpy2/rinterface/__init__.py:145: RRuntimeWarning: warnings.warn(x, RRuntimeWarning) /users3/cse/amfrye/.local/lib/python3.5/site-packages/rpy2/rinterface/__init__.py:145: RRuntimeWarning: The downloaded source packages are in ‘/tmp/RtmpivmiIF/downloaded_packages’ warnings.warn(x, RRuntimeWarning) /users3/cse/amfrye/.local/lib/python3.5/site-packages/rpy2/rinterface/__init__.py:145: RRuntimeWarning: Loading required package: car warnings.warn(x, RRuntimeWarning)
--- Please select a CRAN mirror for use in this session --- Secure CRAN mirrors 1: 0-Cloud [https] 2: Algeria [https] 3: Australia (Canberra) [https] 4: Australia (Melbourne 1) [https] 5: Australia (Melbourne 2) [https] 6: Australia (Perth) [https] 7: Austria [https] 8: Belgium (Ghent) [https] 9: Brazil (PR) [https] 10: Brazil (RJ) [https] 11: Brazil (SP 1) [https] 12: Brazil (SP 2) [https] 13: Bulgaria [https] 14: Chile 1 [https] 15: Chile 2 [https] 16: China (Guangzhou) [https] 17: China (Lanzhou) [https] 18: Colombia (Cali) [https] 19: Czech Republic [https] 20: Denmark [https] 21: Ecuador (Cuenca) [https] 22: Estonia [https] 23: France (Lyon 1) [https] 24: France (Lyon 2) [https] 25: France (Marseille) [https] 26: France (Montpellier) [https] 27: France (Paris 2) [https] 28: Germany (Göttingen) [https] 29: Germany (Münster) [https] 30: Greece [https] 31: Iceland [https] 32: Indonesia (Jakarta) [https] 33: Ireland [https] 34: Italy (Padua) [https] 35: Japan (Tokyo) [https] 36: Malaysia [https] 37: Mexico (Mexico City) [https] 38: Norway [https] 39: Philippines [https] 40: Serbia [https] 41: Spain (A Coruña) [https] 42: Spain (Madrid) [https] 43: Sweden [https] 44: Switzerland [https] 45: Turkey (Denizli) [https] 46: Turkey (Mersin) [https] 47: UK (Bristol) [https] 48: UK (Cambridge) [https] 49: UK (London 1) [https] 50: USA (CA 1) [https] 51: USA (IA) [https] 52: USA (KS) [https] 53: USA (MI 1) [https] 54: USA (OR) [https] 55: USA (TN) [https] 56: USA (TX 1) [https] 57: Vietnam [https] 58: (other mirrors) 'data.frame': 14920 obs. of 100 variables: $ SEP : chr "NS" "NS" "NS" "NS" ... $ GSEGRD : num 11 12 11 12 13 12 13 13 11 11 ... $ IndAvgSalary : num 65898 81219 65898 82168 121939 ... $ SalaryOverUnderIndAvg : num -4041 -9406 -2807 -6547 -26020 ... $ LowerLimitAge : num 20 25 25 25 25 25 25 25 25 25 ... $ YearsToRetirement : num 37 32 32 32 32 32 32 32 32 32 ... $ BLS_FEDERAL_OtherSep_Rate : num 0.4 0.4 0.4 0.4 0.4 0.4 0.4 0.4 0.4 0.4 ... $ BLS_FEDERAL_Quits_Rate : num 0.4 0.4 0.4 0.4 0.4 0.4 0.4 0.4 0.4 0.4 ... $ BLS_FEDERAL_TotalSep_Level : int 34 34 34 34 34 34 34 34 34 34 ... $ BLS_FEDERAL_JobOpenings_Rate : num 2.1 2.1 2.1 2.1 2.1 2.1 2.1 2.1 2.1 2.1 ... $ BLS_FEDERAL_OtherSep_Level : int 10 10 10 10 10 10 10 10 10 10 ... $ BLS_FEDERAL_Quits_Level : int 11 11 11 11 11 11 11 11 11 11 ... $ BLS_FEDERAL_JobOpenings_Level: int 58 58 58 58 58 58 58 58 58 58 ... $ BLS_FEDERAL_Layoffs_Rate : num 0.5 0.5 0.5 0.5 0.5 0.5 0.5 0.5 0.5 0.5 ... $ BLS_FEDERAL_Layoffs_Level : int 13 13 13 13 13 13 13 13 13 13 ... $ BLS_FEDERAL_TotalSep_Rate : num 1.2 1.2 1.2 1.2 1.2 1.2 1.2 1.2 1.2 1.2 ... $ SALARYLog : num 11 11.2 11.1 11.2 11.5 ... $ LOSSqrt : num 2.17 2.68 2 2.41 1.41 ... $ SEPCount_EFDATE_OCCLog : num 5.82 5.82 5.82 4.32 4.14 ... $ SEPCount_EFDATE_LOCLog : num 6.15 6.24 6.83 6.83 6.83 ... $ IndAvgSalaryLog : num 11.1 11.3 11.1 11.3 11.7 ... $ AGELVL_B : chr "1" "0" "0" "0" ... $ AGELVL_C : chr "0" "1" "1" "1" ... $ AGELVL_D : chr "0" "0" "0" "0" ... $ AGELVL_E : chr "0" "0" "0" "0" ... $ AGELVL_F : chr "0" "0" "0" "0" ... $ AGELVL_G : chr "0" "0" "0" "0" ... $ AGELVL_H : chr "0" "0" "0" "0" ... $ AGELVL_I : chr "0" "0" "0" "0" ... $ AGELVL_J : chr "0" "0" "0" "0" ... $ AGELVL_K : chr "0" "0" "0" "0" ... $ LOC_01 : chr "0" "0" "0" "0" ... $ LOC_02 : chr "0" "0" "0" "0" ... $ LOC_04 : chr "0" "0" "0" "0" ... $ LOC_05 : chr "0" "0" "0" "0" ... $ LOC_06 : chr "0" "0" "0" "0" ... $ LOC_08 : chr "0" "0" "0" "0" ... $ LOC_09 : chr "0" "0" "0" "0" ... $ LOC_10 : chr "0" "0" "0" "0" ... $ LOC_11 : chr "0" "0" "0" "0" ... $ LOC_12 : chr "0" "0" "0" "0" ... $ LOC_13 : chr "0" "0" "0" "0" ... $ LOC_15 : chr "0" "0" "0" "0" ... $ LOC_16 : chr "0" "0" "0" "0" ... $ LOC_17 : chr "0" "0" "0" "0" ... $ LOC_18 : chr "0" "0" "0" "0" ... $ LOC_19 : chr "0" "0" "0" "0" ... $ LOC_20 : chr "0" "0" "0" "0" ... $ LOC_21 : chr "0" "0" "0" "0" ... $ LOC_22 : chr "0" "0" "0" "0" ... $ LOC_23 : chr "0" "0" "0" "0" ... $ LOC_24 : chr "0" "0" "1" "1" ... $ LOC_25 : chr "0" "0" "0" "0" ... $ LOC_26 : chr "0" "0" "0" "0" ... $ LOC_27 : chr "0" "0" "0" "0" ... $ LOC_28 : chr "0" "0" "0" "0" ... $ LOC_29 : chr "0" "0" "0" "0" ... $ LOC_30 : chr "0" "0" "0" "0" ... $ LOC_31 : chr "0" "0" "0" "0" ... $ LOC_32 : chr "0" "0" "0" "0" ... $ LOC_33 : chr "0" "0" "0" "0" ... $ LOC_34 : chr "0" "0" "0" "0" ... $ LOC_35 : chr "0" "0" "0" "0" ... $ LOC_36 : chr "0" "0" "0" "0" ... $ LOC_37 : chr "0" "0" "0" "0" ... $ LOC_38 : chr "0" "0" "0" "0" ... $ LOC_39 : chr "0" "0" "0" "0" ... $ LOC_40 : chr "0" "0" "0" "0" ... $ LOC_41 : chr "0" "0" "0" "0" ... $ LOC_42 : chr "1" "0" "0" "0" ... $ LOC_44 : chr "0" "0" "0" "0" ... $ LOC_45 : chr "0" "0" "0" "0" ... $ LOC_46 : chr "0" "0" "0" "0" ... $ LOC_47 : chr "0" "0" "0" "0" ... $ LOC_48 : chr "0" "0" "0" "0" ... $ LOC_49 : chr "0" "1" "0" "0" ... $ LOC_50 : chr "0" "0" "0" "0" ... $ LOC_51 : chr "0" "0" "0" "0" ... $ LOC_53 : chr "0" "0" "0" "0" ... $ LOC_54 : chr "0" "0" "0" "0" ... $ LOC_55 : chr "0" "0" "0" "0" ... $ LOC_56 : chr "0" "0" "0" "0" ... $ TOA_10 : chr "1" "1" "0" "1" ... $ TOA_15 : chr "0" "0" "0" "0" ... $ TOA_20 : chr "0" "0" "0" "0" ... $ TOA_30 : chr "0" "0" "0" "0" ... $ TOA_32 : chr "0" "0" "0" "0" ... $ TOA_35 : chr "0" "0" "0" "0" ... $ TOA_38 : chr "0" "0" "1" "0" ... $ TOA_40 : chr "0" "0" "0" "0" ... $ TOA_42 : chr "0" "0" "0" "0" ... $ TOA_44 : chr "0" "0" "0" "0" ... $ TOA_45 : chr "0" "0" "0" "0" ... $ TOA_48 : chr "0" "0" "0" "0" ... $ LOCTYP_1 : chr "1" "1" "1" "1" ... $ PPTYP_1 : chr "1" "1" "1" "1" ... $ PPGROUP_11 : chr "1" "1" "1" "1" ... $ PPGROUP_12 : chr "0" "0" "0" "0" ... $ TOATYP_1 : chr "1" "1" "1" "1" ... [list output truncated] [1] "GSEGRD" "IndAvgSalary" [3] "SalaryOverUnderIndAvg" "LowerLimitAge" [5] "YearsToRetirement" "BLS_FEDERAL_OtherSep_Rate" [7] "BLS_FEDERAL_Quits_Rate" "BLS_FEDERAL_TotalSep_Level" [9] "BLS_FEDERAL_JobOpenings_Rate" "BLS_FEDERAL_OtherSep_Level" [11] "BLS_FEDERAL_Quits_Level" "BLS_FEDERAL_JobOpenings_Level" [13] "BLS_FEDERAL_Layoffs_Rate" "BLS_FEDERAL_Layoffs_Level" [15] "BLS_FEDERAL_TotalSep_Rate" "SALARYLog" [17] "LOSSqrt" "SEPCount_EFDATE_OCCLog" [19] "SEPCount_EFDATE_LOCLog" "IndAvgSalaryLog" [21] "AGELVL_B" "AGELVL_C" [23] "AGELVL_D" "AGELVL_E" [25] "AGELVL_F" "AGELVL_G" [27] "AGELVL_H" "AGELVL_I" [29] "AGELVL_J" "AGELVL_K" [31] "LOC_01" "LOC_02" [33] "LOC_04" "LOC_05" [35] "LOC_06" "LOC_08" [37] "LOC_09" "LOC_10" [39] "LOC_11" "LOC_12" [41] "LOC_13" "LOC_15" [43] "LOC_16" "LOC_17" [45] "LOC_18" "LOC_19" [47] "LOC_20" "LOC_21" [49] "LOC_22" "LOC_23" [51] "LOC_24" "LOC_25" [53] "LOC_26" "LOC_27" [55] "LOC_28" "LOC_29" [57] "LOC_30" "LOC_31" [59] "LOC_32" "LOC_33" [61] "LOC_34" "LOC_35" [63] "LOC_36" "LOC_37" [65] "LOC_38" "LOC_39" [67] "LOC_40" "LOC_41" [69] "LOC_42" "LOC_44" [71] "LOC_45" "LOC_46" [73] "LOC_47" "LOC_48" [75] "LOC_49" "LOC_50" [77] "LOC_51" "LOC_53" [79] "LOC_54" "LOC_55" [81] "LOC_56" "TOA_10" [83] "TOA_15" "TOA_20" [85] "TOA_30" "TOA_32" [87] "TOA_35" "TOA_38" [89] "TOA_40" "TOA_42" [91] "TOA_44" "TOA_45" [93] "TOA_48" "LOCTYP_1" [95] "PPTYP_1" "PPGROUP_11" [97] "PPGROUP_12" "TOATYP_1" [99] "TOATYP_2" [1] "# of SEP observations = 14920" [1] "SEP type = 0" "SEP type = 1" [1] "SEP class = integer"
LOCTYP_1 and PPTYP_1 have only single level and need removed:
%R sapply(OPMAnalysisDataNoFamBinary, function(x) length(unique(x[!is.na(x)])))
array([ 2, 9, 1878, 10766, 10, 10, 4, 4, 9,
10, 8, 6, 10, 4, 7, 6, 3537, 466,
180, 399, 1878, 2, 2, 2, 2, 2, 2,
2, 2, 2, 2, 2, 2, 2, 2, 2,
2, 2, 2, 2, 2, 2, 2, 2, 2,
2, 2, 2, 2, 2, 2, 2, 2, 2,
2, 2, 2, 2, 2, 2, 2, 2, 2,
2, 2, 2, 2, 2, 2, 2, 2, 2,
2, 2, 2, 2, 2, 2, 2, 2, 2,
2, 2, 2, 2, 2, 2, 2, 2, 2,
2, 2, 2, 2, 1, 1, 2, 2, 2, 2], dtype=int32)
Unique counts with LOCTYP_1 and PPTYP_1 removed:
%R sapply(OPMAnalysisDataNoFamBinary[,-c(95,96)], function(x) length(unique(x[!is.na(x)])))
array([ 2, 9, 1878, 10766, 10, 10, 4, 4, 9,
10, 8, 6, 10, 4, 7, 6, 3537, 466,
180, 399, 1878, 2, 2, 2, 2, 2, 2,
2, 2, 2, 2, 2, 2, 2, 2, 2,
2, 2, 2, 2, 2, 2, 2, 2, 2,
2, 2, 2, 2, 2, 2, 2, 2, 2,
2, 2, 2, 2, 2, 2, 2, 2, 2,
2, 2, 2, 2, 2, 2, 2, 2, 2,
2, 2, 2, 2, 2, 2, 2, 2, 2,
2, 2, 2, 2, 2, 2, 2, 2, 2,
2, 2, 2, 2, 2, 2, 2, 2], dtype=int32)
Data example with LOCTYP_1 and PPTYP_1 removed:
%R OPMAnalysisDataNoFamBinary[,-c(95,96)]
| SEP | GSEGRD | IndAvgSalary | SalaryOverUnderIndAvg | LowerLimitAge | YearsToRetirement | BLS_FEDERAL_OtherSep_Rate | BLS_FEDERAL_Quits_Rate | BLS_FEDERAL_TotalSep_Level | BLS_FEDERAL_JobOpenings_Rate | BLS_FEDERAL_OtherSep_Level | BLS_FEDERAL_Quits_Level | BLS_FEDERAL_JobOpenings_Level | BLS_FEDERAL_Layoffs_Rate | BLS_FEDERAL_Layoffs_Level | BLS_FEDERAL_TotalSep_Rate | SALARYLog | LOSSqrt | SEPCount_EFDATE_OCCLog | SEPCount_EFDATE_LOCLog | IndAvgSalaryLog | AGELVL_B | AGELVL_C | AGELVL_D | AGELVL_E | AGELVL_F | AGELVL_G | AGELVL_H | AGELVL_I | AGELVL_J | AGELVL_K | LOC_01 | LOC_02 | LOC_04 | LOC_05 | LOC_06 | LOC_08 | LOC_09 | LOC_10 | LOC_11 | LOC_12 | LOC_13 | LOC_15 | LOC_16 | LOC_17 | LOC_18 | LOC_19 | LOC_20 | LOC_21 | LOC_22 | LOC_23 | LOC_24 | LOC_25 | LOC_26 | LOC_27 | LOC_28 | LOC_29 | LOC_30 | LOC_31 | LOC_32 | LOC_33 | LOC_34 | LOC_35 | LOC_36 | LOC_37 | LOC_38 | LOC_39 | LOC_40 | LOC_41 | LOC_42 | LOC_44 | LOC_45 | LOC_46 | LOC_47 | LOC_48 | LOC_49 | LOC_50 | LOC_51 | LOC_53 | LOC_54 | LOC_55 | LOC_56 | TOA_10 | TOA_15 | TOA_20 | TOA_30 | TOA_32 | TOA_35 | TOA_38 | TOA_40 | TOA_42 | TOA_44 | TOA_45 | TOA_48 | PPGROUP_11 | PPGROUP_12 | TOATYP_1 | TOATYP_2 | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | NS | 11.0 | 65898.205859 | -4041.205859 | 20.0 | 37.0 | 0.4 | 0.4 | 34 | 2.1 | 10 | 11 | 58 | 0.5 | 13 | 1.2 | 11.032581 | 2.167948 | 5.817111 | 6.152733 | 11.095866 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 0 |
| 1 | NS | 12.0 | 81218.917413 | -9405.917413 | 25.0 | 32.0 | 0.4 | 0.4 | 34 | 2.1 | 10 | 11 | 58 | 0.5 | 13 | 1.2 | 11.181821 | 2.683282 | 5.817111 | 6.240276 | 11.304903 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 0 |
| 2 | NS | 11.0 | 65898.205859 | -2807.205859 | 25.0 | 32.0 | 0.4 | 0.4 | 34 | 2.1 | 10 | 11 | 58 | 0.5 | 13 | 1.2 | 11.052333 | 2.000000 | 5.817111 | 6.827629 | 11.095866 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 0 |
| 3 | NS | 12.0 | 82168.243394 | -6547.243394 | 25.0 | 32.0 | 0.4 | 0.4 | 34 | 2.1 | 10 | 11 | 58 | 0.5 | 13 | 1.2 | 11.233489 | 2.408319 | 4.317488 | 6.827629 | 11.316524 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 0 |
| 4 | NS | 13.0 | 121938.733696 | -26019.733696 | 25.0 | 32.0 | 0.4 | 0.4 | 34 | 2.1 | 10 | 11 | 58 | 0.5 | 13 | 1.2 | 11.471259 | 1.414214 | 4.143135 | 6.827629 | 11.711274 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 0 |
| 5 | NS | 12.0 | 86658.133166 | -5996.133166 | 25.0 | 32.0 | 0.4 | 0.4 | 34 | 2.1 | 10 | 11 | 58 | 0.5 | 13 | 1.2 | 11.298023 | 2.302173 | 3.135494 | 7.095064 | 11.369726 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 0 |
| 6 | NS | 13.0 | 102367.108324 | -12443.108324 | 25.0 | 32.0 | 0.4 | 0.4 | 34 | 2.1 | 10 | 11 | 58 | 0.5 | 13 | 1.2 | 11.406720 | 2.213594 | 3.332205 | 6.827629 | 11.536321 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 0 |
| 7 | NS | 13.0 | 90154.615385 | -230.615385 | 25.0 | 32.0 | 0.4 | 0.4 | 34 | 2.1 | 10 | 11 | 58 | 0.5 | 13 | 1.2 | 11.406720 | 1.000000 | 1.609438 | 7.095064 | 11.409281 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 1 | 0 | 0 | 1 |
| 8 | NS | 11.0 | 63268.197674 | -177.197674 | 25.0 | 32.0 | 0.4 | 0.4 | 34 | 2.1 | 10 | 11 | 58 | 0.5 | 13 | 1.2 | 11.052333 | 1.449138 | 3.332205 | 7.021976 | 11.055138 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 0 |
| 9 | NS | 11.0 | 68550.998470 | 3364.001530 | 25.0 | 32.0 | 0.4 | 0.4 | 34 | 2.1 | 10 | 11 | 58 | 0.5 | 13 | 1.2 | 11.183240 | 2.280351 | 4.174387 | 5.117994 | 11.135333 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 0 |
| 10 | NS | 12.0 | 115004.907303 | -11518.907303 | 25.0 | 32.0 | 0.4 | 0.4 | 34 | 2.1 | 10 | 11 | 58 | 0.5 | 13 | 1.2 | 11.547192 | 1.949359 | 4.143135 | 6.827629 | 11.652730 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 0 |
| 11 | NS | 12.0 | 81888.167064 | -3746.167064 | 25.0 | 32.0 | 0.4 | 0.4 | 34 | 2.1 | 10 | 11 | 58 | 0.5 | 13 | 1.2 | 11.266283 | 2.073644 | 3.332205 | 6.827629 | 11.313110 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 0 |
| 12 | NS | 11.0 | 65898.205859 | -3670.205859 | 25.0 | 32.0 | 0.4 | 0.4 | 34 | 2.1 | 10 | 11 | 58 | 0.5 | 13 | 1.2 | 11.038560 | 0.707107 | 5.817111 | 6.698268 | 11.095866 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 0 |
| 13 | NS | 12.0 | 82676.532609 | -4534.532609 | 25.0 | 32.0 | 0.4 | 0.4 | 34 | 2.1 | 10 | 11 | 58 | 0.5 | 13 | 1.2 | 11.266283 | 2.049390 | 4.442651 | 7.021976 | 11.322691 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 0 |
| 14 | NS | 9.0 | 52460.545881 | -4537.545881 | 25.0 | 32.0 | 0.4 | 0.4 | 34 | 2.1 | 10 | 11 | 58 | 0.5 | 13 | 1.2 | 10.777351 | 1.612452 | 5.817111 | 6.212606 | 10.867817 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 0 |
| 15 | NS | 11.0 | 68157.694737 | -1420.694737 | 25.0 | 32.0 | 0.4 | 0.4 | 34 | 2.1 | 10 | 11 | 58 | 0.5 | 13 | 1.2 | 11.108515 | 2.073644 | 4.025352 | 7.563201 | 11.129579 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 0 |
| 16 | NS | 11.0 | 71578.443548 | -11663.443548 | 25.0 | 32.0 | 0.4 | 0.4 | 34 | 2.1 | 10 | 11 | 58 | 0.5 | 13 | 1.2 | 11.000682 | 1.048809 | 1.098612 | 6.390241 | 11.178549 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 1 |
| 17 | NS | 11.0 | 63762.518234 | -1895.518234 | 25.0 | 32.0 | 0.4 | 0.4 | 34 | 2.1 | 10 | 11 | 58 | 0.5 | 13 | 1.2 | 11.032742 | 1.516575 | 4.442651 | 6.716595 | 11.062921 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 0 |
| 18 | NS | 9.0 | 52460.545881 | -4537.545881 | 25.0 | 32.0 | 0.4 | 0.4 | 34 | 2.1 | 10 | 11 | 58 | 0.5 | 13 | 1.2 | 10.777351 | 2.323790 | 5.817111 | 5.686975 | 10.867817 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 0 |
| 19 | NS | 12.0 | 81218.917413 | -3526.917413 | 25.0 | 32.0 | 0.4 | 0.4 | 34 | 2.1 | 10 | 11 | 58 | 0.5 | 13 | 1.2 | 11.260508 | 2.720294 | 5.817111 | 5.476464 | 11.304903 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 0 |
| 20 | NS | 7.0 | 45637.723577 | -1022.723577 | 25.0 | 32.0 | 0.4 | 0.4 | 34 | 2.1 | 10 | 11 | 58 | 0.5 | 13 | 1.2 | 10.705825 | 1.048809 | 4.174387 | 6.240276 | 10.728490 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 0 |
| 21 | NS | 10.0 | 69914.863982 | 5023.136018 | 30.0 | 27.0 | 0.4 | 0.4 | 34 | 2.1 | 10 | 11 | 58 | 0.5 | 13 | 1.2 | 11.224416 | 2.024846 | 6.452049 | 5.564520 | 11.155034 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 0 |
| 22 | NS | 12.0 | 81218.917413 | -4146.917413 | 30.0 | 27.0 | 0.4 | 0.4 | 34 | 2.1 | 10 | 11 | 58 | 0.5 | 13 | 1.2 | 11.252495 | 1.378405 | 5.817111 | 6.698268 | 11.304903 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 0 |
| 23 | NS | 12.0 | 82168.243394 | -124.243394 | 30.0 | 27.0 | 0.4 | 0.4 | 34 | 2.1 | 10 | 11 | 58 | 0.5 | 13 | 1.2 | 11.315011 | 3.361547 | 4.317488 | 6.698268 | 11.316524 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 0 |
| 24 | NS | 15.0 | 145444.410714 | -16283.410714 | 30.0 | 27.0 | 0.4 | 0.4 | 34 | 2.1 | 10 | 11 | 58 | 0.5 | 13 | 1.2 | 11.768815 | 2.569047 | 3.332205 | 7.095064 | 11.887549 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 0 |
| 25 | NS | 13.0 | 98471.000502 | -5549.000502 | 30.0 | 27.0 | 0.4 | 0.4 | 34 | 2.1 | 10 | 11 | 58 | 0.5 | 13 | 1.2 | 11.439516 | 2.280351 | 5.817111 | 6.827629 | 11.497517 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 0 |
| 26 | NS | 11.0 | 65898.205859 | -6392.205859 | 30.0 | 27.0 | 0.4 | 0.4 | 34 | 2.1 | 10 | 11 | 58 | 0.5 | 13 | 1.2 | 10.993832 | 1.643168 | 5.817111 | 5.793014 | 11.095866 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 0 |
| 27 | NS | 13.0 | 98471.000502 | 444.999498 | 30.0 | 27.0 | 0.4 | 0.4 | 34 | 2.1 | 10 | 11 | 58 | 0.5 | 13 | 1.2 | 11.502026 | 3.130495 | 5.817111 | 7.095064 | 11.497517 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 0 |
| 28 | NS | 14.0 | 122479.704518 | -12675.704518 | 30.0 | 27.0 | 0.4 | 0.4 | 34 | 2.1 | 10 | 11 | 58 | 0.5 | 13 | 1.2 | 11.606452 | 3.049590 | 5.176150 | 7.095064 | 11.715701 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 0 |
| 29 | NS | 12.0 | 86919.780702 | -4875.780702 | 30.0 | 27.0 | 0.4 | 0.4 | 34 | 2.1 | 10 | 11 | 58 | 0.5 | 13 | 1.2 | 11.315011 | 2.588436 | 2.197225 | 6.698268 | 11.372741 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 0 |
| 30 | NS | 9.0 | 52460.545881 | 3161.454119 | 30.0 | 27.0 | 0.4 | 0.4 | 34 | 2.1 | 10 | 11 | 58 | 0.5 | 13 | 1.2 | 10.926334 | 1.732051 | 5.817111 | 7.095064 | 10.867817 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 0 |
| 31 | NS | 11.0 | 68533.372995 | -8618.372995 | 30.0 | 27.0 | 0.4 | 0.4 | 34 | 2.1 | 10 | 11 | 58 | 0.5 | 13 | 1.2 | 11.000682 | 1.516575 | 3.555348 | 7.563201 | 11.135076 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 0 |
| 32 | NS | 12.0 | 81218.917413 | -4347.917413 | 30.0 | 27.0 | 0.4 | 0.4 | 34 | 2.1 | 10 | 11 | 58 | 0.5 | 13 | 1.2 | 11.249884 | 2.449490 | 5.817111 | 7.563201 | 11.304903 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 0 |
| 33 | NS | 11.0 | 69997.224535 | 3548.775465 | 30.0 | 27.0 | 0.4 | 0.4 | 34 | 2.1 | 10 | 11 | 58 | 0.5 | 13 | 1.2 | 11.205666 | 3.301515 | 3.044522 | 6.900731 | 11.156211 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 0 |
| 34 | NS | 15.0 | 148432.131584 | -15104.131584 | 30.0 | 27.0 | 0.4 | 0.4 | 34 | 2.1 | 10 | 11 | 58 | 0.5 | 13 | 1.2 | 11.800568 | 2.469818 | 5.176150 | 7.095064 | 11.907883 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 0 |
| 35 | NS | 12.0 | 81218.917413 | -5597.917413 | 30.0 | 27.0 | 0.4 | 0.4 | 34 | 2.1 | 10 | 11 | 58 | 0.5 | 13 | 1.2 | 11.233489 | 2.144761 | 5.817111 | 6.827629 | 11.304903 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 0 |
| 36 | NS | 12.0 | 86552.911802 | -3157.911802 | 30.0 | 27.0 | 0.4 | 0.4 | 34 | 2.1 | 10 | 11 | 58 | 0.5 | 13 | 1.2 | 11.331344 | 3.646917 | 4.174387 | 6.745236 | 11.368511 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 0 |
| 37 | NS | 11.0 | 65898.205859 | 5605.794141 | 30.0 | 27.0 | 0.4 | 0.4 | 34 | 2.1 | 10 | 11 | 58 | 0.5 | 13 | 1.2 | 11.177509 | 2.073644 | 5.817111 | 7.021976 | 11.095866 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 0 |
| 38 | NS | 14.0 | 122479.704518 | -12675.704518 | 30.0 | 27.0 | 0.4 | 0.4 | 34 | 2.1 | 10 | 11 | 58 | 0.5 | 13 | 1.2 | 11.606452 | 1.673320 | 5.176150 | 7.095064 | 11.715701 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 1 |
| 39 | NS | 9.0 | 52460.545881 | 919.454119 | 30.0 | 27.0 | 0.4 | 0.4 | 34 | 2.1 | 10 | 11 | 58 | 0.5 | 13 | 1.2 | 10.885191 | 2.489980 | 5.817111 | 7.563201 | 10.867817 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 0 |
| 40 | NS | 9.0 | 52460.545881 | -2940.545881 | 30.0 | 27.0 | 0.4 | 0.4 | 34 | 2.1 | 10 | 11 | 58 | 0.5 | 13 | 1.2 | 10.810132 | 2.387467 | 5.817111 | 6.390241 | 10.867817 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 0 |
| 41 | NS | 11.0 | 65898.205859 | 3836.794141 | 30.0 | 27.0 | 0.4 | 0.4 | 34 | 2.1 | 10 | 11 | 58 | 0.5 | 13 | 1.2 | 11.152458 | 1.949359 | 5.817111 | 6.265301 | 11.095866 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 0 |
| 42 | NS | 12.0 | 82676.532609 | 506.467391 | 30.0 | 27.0 | 0.4 | 0.4 | 34 | 2.1 | 10 | 11 | 58 | 0.5 | 13 | 1.2 | 11.328798 | 2.529822 | 4.442651 | 7.021976 | 11.322691 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 0 |
| 43 | NS | 12.0 | 81218.917413 | -1021.917413 | 30.0 | 27.0 | 0.4 | 0.4 | 34 | 2.1 | 10 | 11 | 58 | 0.5 | 13 | 1.2 | 11.292241 | 3.178050 | 5.817111 | 5.793014 | 11.304903 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 0 |
| 44 | NS | 9.0 | 52460.545881 | 1339.454119 | 30.0 | 27.0 | 0.4 | 0.4 | 34 | 2.1 | 10 | 11 | 58 | 0.5 | 13 | 1.2 | 10.893029 | 3.016621 | 5.817111 | 5.337538 | 10.867817 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 0 |
| 45 | NS | 14.0 | 119824.704879 | 7687.295121 | 30.0 | 27.0 | 0.4 | 0.4 | 34 | 2.1 | 10 | 11 | 58 | 0.5 | 13 | 1.2 | 11.755966 | 1.414214 | 5.817111 | 7.095064 | 11.693785 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 0 |
| 46 | NS | 11.0 | 65898.205859 | 1657.794141 | 30.0 | 27.0 | 0.4 | 0.4 | 34 | 2.1 | 10 | 11 | 58 | 0.5 | 13 | 1.2 | 11.120712 | 1.974842 | 5.817111 | 6.265301 | 11.095866 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 0 |
| 47 | NS | 9.0 | 52460.545881 | 6860.454119 | 30.0 | 27.0 | 0.4 | 0.4 | 34 | 2.1 | 10 | 11 | 58 | 0.5 | 13 | 1.2 | 10.990719 | 2.645751 | 5.817111 | 5.564520 | 10.867817 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 0 |
| 48 | NS | 9.0 | 52460.545881 | 3161.454119 | 30.0 | 27.0 | 0.4 | 0.4 | 34 | 2.1 | 10 | 11 | 58 | 0.5 | 13 | 1.2 | 10.926334 | 1.483240 | 5.817111 | 6.827629 | 10.867817 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 0 |
| 49 | NS | 12.0 | 81218.917413 | 9525.082587 | 30.0 | 27.0 | 0.4 | 0.4 | 34 | 2.1 | 10 | 11 | 58 | 0.5 | 13 | 1.2 | 11.415798 | 1.732051 | 5.817111 | 7.021976 | 11.304903 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 0 |
| 50 | NS | 14.0 | 122479.704518 | -12675.704518 | 30.0 | 27.0 | 0.4 | 0.4 | 34 | 2.1 | 10 | 11 | 58 | 0.5 | 13 | 1.2 | 11.606452 | 1.414214 | 5.176150 | 7.095064 | 11.715701 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 0 |
| 51 | NS | 12.0 | 81218.917413 | -3076.917413 | 35.0 | 22.0 | 0.4 | 0.4 | 34 | 2.1 | 10 | 11 | 58 | 0.5 | 13 | 1.2 | 11.266283 | 2.345208 | 5.817111 | 7.095064 | 11.304903 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 0 |
| 52 | NS | 14.0 | 122479.704518 | -5592.704518 | 35.0 | 22.0 | 0.4 | 0.4 | 34 | 2.1 | 10 | 11 | 58 | 0.5 | 13 | 1.2 | 11.668963 | 2.588436 | 5.176150 | 7.021976 | 11.715701 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 0 |
| 53 | NS | 15.0 | 148432.131584 | -15104.131584 | 35.0 | 22.0 | 0.4 | 0.4 | 34 | 2.1 | 10 | 11 | 58 | 0.5 | 13 | 1.2 | 11.800568 | 1.949359 | 5.176150 | 7.021976 | 11.907883 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 0 |
| 54 | NS | 13.0 | 100441.761115 | -4522.761115 | 35.0 | 22.0 | 0.4 | 0.4 | 34 | 2.1 | 10 | 11 | 58 | 0.5 | 13 | 1.2 | 11.471259 | 3.178050 | 4.317488 | 7.095064 | 11.517333 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 0 |
| 55 | NS | 11.0 | 67298.007653 | -7383.007653 | 35.0 | 22.0 | 0.4 | 0.4 | 34 | 2.1 | 10 | 11 | 58 | 0.5 | 13 | 1.2 | 11.000682 | 1.264911 | 4.718499 | 6.152733 | 11.116886 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 0 |
| 56 | NS | 13.0 | 98471.000502 | -5549.000502 | 35.0 | 22.0 | 0.4 | 0.4 | 34 | 2.1 | 10 | 11 | 58 | 0.5 | 13 | 1.2 | 11.439516 | 3.255764 | 5.817111 | 7.095064 | 11.497517 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 0 |
| 57 | NS | 12.0 | 82676.532609 | -4534.532609 | 35.0 | 22.0 | 0.4 | 0.4 | 34 | 2.1 | 10 | 11 | 58 | 0.5 | 13 | 1.2 | 11.266283 | 2.144761 | 4.442651 | 7.021976 | 11.322691 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 0 |
| 58 | NS | 13.0 | 104301.705607 | 609.294393 | 35.0 | 22.0 | 0.4 | 0.4 | 34 | 2.1 | 10 | 11 | 58 | 0.5 | 13 | 1.2 | 11.560868 | 3.000000 | 4.718499 | 7.021976 | 11.555043 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 1 | 0 | 0 | 1 |
| 59 | NS | 13.0 | 100116.976930 | 1797.023070 | 35.0 | 22.0 | 0.4 | 0.4 | 34 | 2.1 | 10 | 11 | 58 | 0.5 | 13 | 1.2 | 11.531885 | 3.633180 | 3.332205 | 7.021976 | 11.514095 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 0 |
| 60 | NS | 9.0 | 53388.082192 | 7447.917808 | 35.0 | 22.0 | 0.4 | 0.4 | 34 | 2.1 | 10 | 11 | 58 | 0.5 | 13 | 1.2 | 11.015937 | 0.547723 | 4.442651 | 7.021976 | 10.885343 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 0 |
| 61 | NS | 13.0 | 98471.000502 | -2552.000502 | 35.0 | 22.0 | 0.4 | 0.4 | 34 | 2.1 | 10 | 11 | 58 | 0.5 | 13 | 1.2 | 11.471259 | 2.323790 | 5.817111 | 7.095064 | 11.497517 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 0 |
| 62 | NS | 11.0 | 65270.545455 | 8823.454545 | 35.0 | 22.0 | 0.4 | 0.4 | 34 | 2.1 | 10 | 11 | 58 | 0.5 | 13 | 1.2 | 11.213090 | 3.240370 | 4.418841 | 6.152733 | 11.086296 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 0 |
| 63 | NS | 15.0 | 148432.131584 | 1560.868416 | 35.0 | 22.0 | 0.4 | 0.4 | 34 | 2.1 | 10 | 11 | 58 | 0.5 | 13 | 1.2 | 11.918344 | 3.130495 | 5.176150 | 7.095064 | 11.907883 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 0 |
| 64 | NS | 13.0 | 104499.288690 | -5760.288690 | 35.0 | 22.0 | 0.4 | 0.4 | 34 | 2.1 | 10 | 11 | 58 | 0.5 | 13 | 1.2 | 11.500235 | 3.361547 | 4.174387 | 6.716595 | 11.556936 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 0 |
| 65 | NS | 12.0 | 85842.527653 | -2070.527653 | 35.0 | 22.0 | 0.4 | 0.4 | 34 | 2.1 | 10 | 11 | 58 | 0.5 | 13 | 1.2 | 11.335854 | 1.897367 | 2.302585 | 5.758902 | 11.360270 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 0 |
| 66 | NS | 14.0 | 124094.109562 | -10748.109562 | 35.0 | 22.0 | 0.4 | 0.4 | 34 | 2.1 | 10 | 11 | 58 | 0.5 | 13 | 1.2 | 11.638200 | 3.130495 | 4.442651 | 7.095064 | 11.728796 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 0 |
| 67 | NS | 14.0 | 122479.704518 | -5592.704518 | 35.0 | 22.0 | 0.4 | 0.4 | 34 | 2.1 | 10 | 11 | 58 | 0.5 | 13 | 1.2 | 11.668963 | 2.280351 | 5.176150 | 7.095064 | 11.715701 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 0 |
| 68 | NS | 11.0 | 68807.640150 | 13521.359850 | 35.0 | 22.0 | 0.4 | 0.4 | 34 | 2.1 | 10 | 11 | 58 | 0.5 | 13 | 1.2 | 11.318479 | 2.569047 | 4.158883 | 5.564520 | 11.139070 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 0 |
| 69 | NS | 12.0 | 81218.917413 | -3076.917413 | 35.0 | 22.0 | 0.4 | 0.4 | 34 | 2.1 | 10 | 11 | 58 | 0.5 | 13 | 1.2 | 11.266283 | 2.000000 | 5.817111 | 6.827629 | 11.304903 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 0 |
| 70 | NS | 12.0 | 84469.248195 | -3807.248195 | 35.0 | 22.0 | 0.4 | 0.4 | 34 | 2.1 | 10 | 11 | 58 | 0.5 | 13 | 1.2 | 11.298023 | 1.414214 | 4.454347 | 6.827629 | 11.344143 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 1 | 0 | 0 | 1 |
| 71 | NS | 14.0 | 122479.704518 | -5592.704518 | 35.0 | 22.0 | 0.4 | 0.4 | 34 | 2.1 | 10 | 11 | 58 | 0.5 | 13 | 1.2 | 11.668963 | 2.915476 | 5.176150 | 7.095064 | 11.715701 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 0 |
| 72 | NS | 14.0 | 123010.788889 | -6123.788889 | 35.0 | 22.0 | 0.4 | 0.4 | 34 | 2.1 | 10 | 11 | 58 | 0.5 | 13 | 1.2 | 11.668963 | 2.529822 | 5.817111 | 7.021976 | 11.720027 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 1 | 0 |
| 73 | NS | 11.0 | 65898.205859 | 19338.794141 | 35.0 | 22.0 | 0.4 | 0.4 | 34 | 2.1 | 10 | 11 | 58 | 0.5 | 13 | 1.2 | 11.353191 | 3.033150 | 5.817111 | 5.686975 | 11.095866 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 0 |
| 74 | NS | 12.0 | 84836.137255 | 5907.862745 | 35.0 | 22.0 | 0.4 | 0.4 | 34 | 2.1 | 10 | 11 | 58 | 0.5 | 13 | 1.2 | 11.415798 | 3.114482 | 3.737670 | 6.827629 | 11.348477 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 0 |
| 75 | NS | 13.0 | 98471.000502 | -8547.000502 | 35.0 | 22.0 | 0.4 | 0.4 | 34 | 2.1 | 10 | 11 | 58 | 0.5 | 13 | 1.2 | 11.406720 | 2.236068 | 5.817111 | 7.095064 | 11.497517 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 0 |
| 76 | NS | 13.0 | 102166.799898 | -2996.799898 | 35.0 | 22.0 | 0.4 | 0.4 | 34 | 2.1 | 10 | 11 | 58 | 0.5 | 13 | 1.2 | 11.504591 | 3.872983 | 4.442651 | 6.075346 | 11.534362 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 0 |
| 77 | NS | 13.0 | 103163.886640 | -4552.886640 | 40.0 | 17.0 | 0.4 | 0.4 | 34 | 2.1 | 10 | 11 | 58 | 0.5 | 13 | 1.2 | 11.498938 | 2.366432 | 1.791759 | 6.212606 | 11.544074 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 0 |
| 78 | NS | 14.0 | 119824.704879 | -2937.704879 | 40.0 | 17.0 | 0.4 | 0.4 | 34 | 2.1 | 10 | 11 | 58 | 0.5 | 13 | 1.2 | 11.668963 | 4.571652 | 5.817111 | 6.827629 | 11.693785 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 0 |
| 79 | NS | 10.0 | 69914.863982 | -4217.863982 | 40.0 | 17.0 | 0.4 | 0.4 | 34 | 2.1 | 10 | 11 | 58 | 0.5 | 13 | 1.2 | 11.092809 | 1.788854 | 6.452049 | 6.716595 | 11.155034 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 0 |
| 80 | NS | 12.0 | 81218.917413 | -3198.917413 | 40.0 | 17.0 | 0.4 | 0.4 | 34 | 2.1 | 10 | 11 | 58 | 0.5 | 13 | 1.2 | 11.264720 | 2.213594 | 5.817111 | 5.337538 | 11.304903 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 0 |
| 81 | NS | 14.0 | 123955.919861 | 10639.080139 | 40.0 | 17.0 | 0.4 | 0.4 | 34 | 2.1 | 10 | 11 | 58 | 0.5 | 13 | 1.2 | 11.810026 | 3.255764 | 3.332205 | 7.021976 | 11.727681 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 0 |
| 82 | NS | 11.0 | 67994.769874 | -6147.769874 | 40.0 | 17.0 | 0.4 | 0.4 | 34 | 2.1 | 10 | 11 | 58 | 0.5 | 13 | 1.2 | 11.032419 | 2.846050 | 2.302585 | 6.698268 | 11.127186 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 0 |
| 83 | NS | 13.0 | 100441.761115 | -10517.761115 | 40.0 | 17.0 | 0.4 | 0.4 | 34 | 2.1 | 10 | 11 | 58 | 0.5 | 13 | 1.2 | 11.406720 | 3.507136 | 4.317488 | 7.095064 | 11.517333 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 0 |
| 84 | NS | 13.0 | 98471.000502 | -5549.000502 | 40.0 | 17.0 | 0.4 | 0.4 | 34 | 2.1 | 10 | 11 | 58 | 0.5 | 13 | 1.2 | 11.439516 | 1.516575 | 5.817111 | 6.827629 | 11.497517 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 0 |
| 85 | NS | 9.0 | 55955.461107 | -3279.461107 | 40.0 | 17.0 | 0.4 | 0.4 | 34 | 2.1 | 10 | 11 | 58 | 0.5 | 13 | 1.2 | 10.871915 | 3.065942 | 4.158883 | 5.877736 | 10.932311 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 0 |
| 86 | NS | 13.0 | 101709.414519 | -10803.414519 | 40.0 | 17.0 | 0.4 | 0.4 | 34 | 2.1 | 10 | 11 | 58 | 0.5 | 13 | 1.2 | 11.417581 | 3.949684 | 4.770685 | 5.620401 | 11.529875 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 0 |
| 87 | NS | 14.0 | 120530.582375 | 6981.417625 | 40.0 | 17.0 | 0.4 | 0.4 | 34 | 2.1 | 10 | 11 | 58 | 0.5 | 13 | 1.2 | 11.755966 | 2.664583 | 3.295837 | 7.095064 | 11.699659 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 0 |
| 88 | NS | 12.0 | 82168.243394 | -3406.243394 | 40.0 | 17.0 | 0.4 | 0.4 | 34 | 2.1 | 10 | 11 | 58 | 0.5 | 13 | 1.2 | 11.274186 | 3.361547 | 4.317488 | 5.921578 | 11.316524 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 0 |
| 89 | NS | 10.0 | 69914.863982 | 12673.136018 | 40.0 | 17.0 | 0.4 | 0.4 | 34 | 2.1 | 10 | 11 | 58 | 0.5 | 13 | 1.2 | 11.321620 | 3.130495 | 6.452049 | 5.337538 | 11.155034 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 0 |
| 90 | NS | 11.0 | 65898.205859 | -1764.205859 | 40.0 | 17.0 | 0.4 | 0.4 | 34 | 2.1 | 10 | 11 | 58 | 0.5 | 13 | 1.2 | 11.068730 | 3.834058 | 5.817111 | 7.563201 | 11.095866 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 0 |
| 91 | NS | 10.0 | 69914.863982 | 14169.136018 | 40.0 | 17.0 | 0.4 | 0.4 | 34 | 2.1 | 10 | 11 | 58 | 0.5 | 13 | 1.2 | 11.339572 | 3.033150 | 6.452049 | 5.347108 | 11.155034 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 0 |
| 92 | NS | 11.0 | 65898.205859 | -4772.205859 | 40.0 | 17.0 | 0.4 | 0.4 | 34 | 2.1 | 10 | 11 | 58 | 0.5 | 13 | 1.2 | 11.020693 | 3.130495 | 5.817111 | 6.716595 | 11.095866 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 0 |
| 93 | NS | 9.0 | 52460.545881 | -2940.545881 | 40.0 | 17.0 | 0.4 | 0.4 | 34 | 2.1 | 10 | 11 | 58 | 0.5 | 13 | 1.2 | 10.810132 | 3.435113 | 5.817111 | 7.021976 | 10.867817 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 0 |
| 94 | NS | 14.0 | 122479.704518 | 8573.295482 | 40.0 | 17.0 | 0.4 | 0.4 | 34 | 2.1 | 10 | 11 | 58 | 0.5 | 13 | 1.2 | 11.783357 | 2.645751 | 5.176150 | 7.095064 | 11.715701 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 0 |
| 95 | NS | 13.0 | 98471.000502 | 6439.999498 | 40.0 | 17.0 | 0.4 | 0.4 | 34 | 2.1 | 10 | 11 | 58 | 0.5 | 13 | 1.2 | 11.560868 | 3.911521 | 5.817111 | 7.021976 | 11.497517 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 0 |
| 96 | NS | 11.0 | 68550.998470 | 1027.001530 | 40.0 | 17.0 | 0.4 | 0.4 | 34 | 2.1 | 10 | 11 | 58 | 0.5 | 13 | 1.2 | 11.150204 | 2.073644 | 4.174387 | 4.890349 | 11.135333 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 0 |
| 97 | NS | 10.0 | 67113.940323 | -8556.940323 | 40.0 | 17.0 | 0.4 | 0.4 | 34 | 2.1 | 10 | 11 | 58 | 0.5 | 13 | 1.2 | 10.977756 | 2.000000 | 3.912023 | 6.212606 | 11.114147 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 0 |
| 98 | NS | 12.0 | 81218.917413 | 2808.082587 | 40.0 | 17.0 | 0.4 | 0.4 | 34 | 2.1 | 10 | 11 | 58 | 0.5 | 13 | 1.2 | 11.338893 | 3.286335 | 5.817111 | 6.152733 | 11.304903 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 0 |
| 99 | NS | 12.0 | 82168.243394 | -4026.243394 | 40.0 | 17.0 | 0.4 | 0.4 | 34 | 2.1 | 10 | 11 | 58 | 0.5 | 13 | 1.2 | 11.266283 | 1.303840 | 4.317488 | 7.021976 | 11.316524 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 0 |
| 100 | NS | 12.0 | 83989.772593 | -5969.772593 | 40.0 | 17.0 | 0.4 | 0.4 | 34 | 2.1 | 10 | 11 | 58 | 0.5 | 13 | 1.2 | 11.264720 | 2.387467 | 4.770685 | 5.337538 | 11.338450 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 0 |
| 101 | NS | 11.0 | 69431.488814 | -9516.488814 | 40.0 | 17.0 | 0.4 | 0.4 | 34 | 2.1 | 10 | 11 | 58 | 0.5 | 13 | 1.2 | 11.000682 | 1.549193 | 4.454347 | 6.265301 | 11.148096 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 0 |
| 102 | NS | 9.0 | 53716.937500 | 14070.062500 | 40.0 | 17.0 | 0.4 | 0.4 | 34 | 2.1 | 10 | 11 | 58 | 0.5 | 13 | 1.2 | 11.124126 | 1.974842 | 2.484907 | 6.827629 | 10.891484 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 0 |
| 103 | NS | 11.0 | 66556.592744 | 2843.407256 | 45.0 | 12.0 | 0.4 | 0.4 | 34 | 2.1 | 10 | 11 | 58 | 0.5 | 13 | 1.2 | 11.147642 | 3.507136 | 4.317488 | 6.827629 | 11.105808 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 0 |
| 104 | NS | 12.0 | 81218.917413 | -139.917413 | 45.0 | 12.0 | 0.4 | 0.4 | 34 | 2.1 | 10 | 11 | 58 | 0.5 | 13 | 1.2 | 11.303179 | 3.271085 | 5.817111 | 5.820083 | 11.304903 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 0 |
| 105 | NS | 12.0 | 81218.917413 | -2456.917413 | 45.0 | 12.0 | 0.4 | 0.4 | 34 | 2.1 | 10 | 11 | 58 | 0.5 | 13 | 1.2 | 11.274186 | 3.209361 | 5.817111 | 6.698268 | 11.304903 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 0 |
| 106 | NS | 12.0 | 81270.257040 | 22942.742960 | 45.0 | 12.0 | 0.4 | 0.4 | 34 | 2.1 | 10 | 11 | 58 | 0.5 | 13 | 1.2 | 11.554192 | 2.683282 | 4.406719 | 7.563201 | 11.305535 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 0 |
| 107 | NS | 12.0 | 81218.917413 | 4484.082587 | 45.0 | 12.0 | 0.4 | 0.4 | 34 | 2.1 | 10 | 11 | 58 | 0.5 | 13 | 1.2 | 11.358643 | 2.738613 | 5.817111 | 7.021976 | 11.304903 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 0 |
| 108 | NS | 11.0 | 68648.288490 | 929.711510 | 45.0 | 12.0 | 0.4 | 0.4 | 34 | 2.1 | 10 | 11 | 58 | 0.5 | 13 | 1.2 | 11.150204 | 3.714835 | 2.302585 | 5.620401 | 11.136751 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 0 |
| 109 | NS | 12.0 | 85224.629066 | 16641.370934 | 45.0 | 12.0 | 0.4 | 0.4 | 34 | 2.1 | 10 | 11 | 58 | 0.5 | 13 | 1.2 | 11.531414 | 2.366432 | 4.718499 | 6.265301 | 11.353046 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 0 |
| 110 | NS | 11.0 | 63762.518234 | -3847.518234 | 45.0 | 12.0 | 0.4 | 0.4 | 34 | 2.1 | 10 | 11 | 58 | 0.5 | 13 | 1.2 | 11.000682 | 1.140175 | 4.442651 | 5.746203 | 11.062921 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 0 |
| 111 | NS | 11.0 | 69588.988002 | 1922.011998 | 45.0 | 12.0 | 0.4 | 0.4 | 34 | 2.1 | 10 | 11 | 58 | 0.5 | 13 | 1.2 | 11.177607 | 4.949747 | 4.770685 | 6.368187 | 11.150362 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 0 |
| 112 | NS | 13.0 | 99334.429280 | -13938.429280 | 45.0 | 12.0 | 0.4 | 0.4 | 34 | 2.1 | 10 | 11 | 58 | 0.5 | 13 | 1.2 | 11.355055 | 2.509980 | 2.397895 | 7.021976 | 11.506248 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 0 |
| 113 | NS | 13.0 | 108903.845146 | -6604.845146 | 45.0 | 12.0 | 0.4 | 0.4 | 34 | 2.1 | 10 | 11 | 58 | 0.5 | 13 | 1.2 | 11.535655 | 3.130495 | 2.890372 | 5.564520 | 11.598221 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 0 |
| 114 | NS | 15.0 | 214400.347826 | -14400.347826 | 45.0 | 12.0 | 0.4 | 0.4 | 34 | 2.1 | 10 | 11 | 58 | 0.5 | 13 | 1.2 | 12.206073 | 0.547723 | 5.666427 | 5.877736 | 12.275600 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 1 | 0 |
| 115 | NS | 13.0 | 102367.108324 | 5541.891676 | 45.0 | 12.0 | 0.4 | 0.4 | 34 | 2.1 | 10 | 11 | 58 | 0.5 | 13 | 1.2 | 11.589044 | 3.924283 | 3.332205 | 6.827629 | 11.536321 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 0 |
| 116 | NS | 11.0 | 68807.640150 | 6568.359850 | 45.0 | 12.0 | 0.4 | 0.4 | 34 | 2.1 | 10 | 11 | 58 | 0.5 | 13 | 1.2 | 11.230244 | 5.029911 | 4.158883 | 5.164786 | 11.139070 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 0 |
| 117 | NS | 14.0 | 119824.704879 | -2937.704879 | 45.0 | 12.0 | 0.4 | 0.4 | 34 | 2.1 | 10 | 11 | 58 | 0.5 | 13 | 1.2 | 11.668963 | 3.049590 | 5.817111 | 7.095064 | 11.693785 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 0 |
| 118 | NS | 13.0 | 98471.000502 | 3442.999498 | 45.0 | 12.0 | 0.4 | 0.4 | 34 | 2.1 | 10 | 11 | 58 | 0.5 | 13 | 1.2 | 11.531885 | 4.743416 | 5.817111 | 7.021976 | 11.497517 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 0 |
| 119 | NS | 11.0 | 78776.670354 | 9073.329646 | 45.0 | 12.0 | 0.4 | 0.4 | 34 | 2.1 | 10 | 11 | 58 | 0.5 | 13 | 1.2 | 11.383386 | 1.341641 | 6.452049 | 7.563201 | 11.274372 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 0 |
| 120 | NS | 14.0 | 124261.774194 | -14457.774194 | 45.0 | 12.0 | 0.4 | 0.4 | 34 | 2.1 | 10 | 11 | 58 | 0.5 | 13 | 1.2 | 11.606452 | 2.509980 | 1.791759 | 7.021976 | 11.730146 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 0 |
| 121 | NS | 14.0 | 184294.595000 | -23091.595000 | 45.0 | 12.0 | 0.4 | 0.4 | 34 | 2.1 | 10 | 11 | 58 | 0.5 | 13 | 1.2 | 11.990420 | 2.549510 | 5.666427 | 6.745236 | 12.124291 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 1 | 0 |
| 122 | NS | 13.0 | 104301.705607 | 3132.294393 | 45.0 | 12.0 | 0.4 | 0.4 | 34 | 2.1 | 10 | 11 | 58 | 0.5 | 13 | 1.2 | 11.584632 | 3.286335 | 4.718499 | 5.758902 | 11.555043 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 0 |
| 123 | NS | 12.0 | 85224.629066 | -8778.629066 | 45.0 | 12.0 | 0.4 | 0.4 | 34 | 2.1 | 10 | 11 | 58 | 0.5 | 13 | 1.2 | 11.244340 | 3.240370 | 4.718499 | 6.698268 | 11.353046 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 0 |
| 124 | NS | 14.0 | 184294.595000 | -1802.595000 | 45.0 | 12.0 | 0.4 | 0.4 | 34 | 2.1 | 10 | 11 | 58 | 0.5 | 13 | 1.2 | 12.114462 | 2.236068 | 5.666427 | 5.420535 | 12.124291 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 1 | 0 |
| 125 | NS | 14.0 | 125853.066493 | -16049.066493 | 45.0 | 12.0 | 0.4 | 0.4 | 34 | 2.1 | 10 | 11 | 58 | 0.5 | 13 | 1.2 | 11.606452 | 2.049390 | 3.135494 | 7.095064 | 11.742870 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 1 | 0 | 0 | 1 |
| 126 | NS | 14.0 | 122526.088857 | -5639.088857 | 45.0 | 12.0 | 0.4 | 0.4 | 34 | 2.1 | 10 | 11 | 58 | 0.5 | 13 | 1.2 | 11.668963 | 2.915476 | 4.317488 | 7.021976 | 11.716079 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 0 |
| 127 | NS | 11.0 | 65898.205859 | 1747.794141 | 45.0 | 12.0 | 0.4 | 0.4 | 34 | 2.1 | 10 | 11 | 58 | 0.5 | 13 | 1.2 | 11.122044 | 3.872983 | 5.817111 | 5.347108 | 11.095866 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 0 |
| 128 | NS | 13.0 | 104927.762299 | 2506.237701 | 45.0 | 12.0 | 0.4 | 0.4 | 34 | 2.1 | 10 | 11 | 58 | 0.5 | 13 | 1.2 | 11.584632 | 5.253570 | 3.713572 | 6.075346 | 11.561027 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 0 |
| 129 | NS | 15.0 | 147495.653509 | 9604.346491 | 45.0 | 12.0 | 0.4 | 0.4 | 34 | 2.1 | 10 | 11 | 58 | 0.5 | 13 | 1.2 | 11.964638 | 4.604346 | 4.158883 | 6.827629 | 11.901554 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 0 |
| 130 | NS | 13.0 | 104574.826233 | -2660.826233 | 50.0 | 7.0 | 0.4 | 0.4 | 34 | 2.1 | 10 | 11 | 58 | 0.5 | 13 | 1.2 | 11.531885 | 1.870829 | 4.418841 | 6.827629 | 11.557658 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 0 |
| 131 | NS | 11.0 | 68807.640150 | 6568.359850 | 50.0 | 7.0 | 0.4 | 0.4 | 34 | 2.1 | 10 | 11 | 58 | 0.5 | 13 | 1.2 | 11.230244 | 4.827007 | 4.158883 | 5.420535 | 11.139070 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 0 |
| 132 | NS | 12.0 | 85767.283082 | -4688.283082 | 50.0 | 7.0 | 0.4 | 0.4 | 34 | 2.1 | 10 | 11 | 58 | 0.5 | 13 | 1.2 | 11.303179 | 1.702939 | 3.091042 | 6.716595 | 11.359393 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 0 |
| 133 | NS | 13.0 | 104301.705607 | 3132.294393 | 50.0 | 7.0 | 0.4 | 0.4 | 34 | 2.1 | 10 | 11 | 58 | 0.5 | 13 | 1.2 | 11.584632 | 5.059644 | 4.718499 | 5.278115 | 11.555043 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 0 |
| 134 | NS | 12.0 | 81218.917413 | -4772.917413 | 50.0 | 7.0 | 0.4 | 0.4 | 34 | 2.1 | 10 | 11 | 58 | 0.5 | 13 | 1.2 | 11.244340 | 5.205766 | 5.817111 | 5.141664 | 11.304903 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 0 |
| 135 | NS | 14.0 | 123955.919861 | 14180.080139 | 50.0 | 7.0 | 0.4 | 0.4 | 34 | 2.1 | 10 | 11 | 58 | 0.5 | 13 | 1.2 | 11.835994 | 3.660601 | 3.332205 | 7.021976 | 11.727681 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 0 |
| 136 | NS | 12.0 | 82676.532609 | -6230.532609 | 50.0 | 7.0 | 0.4 | 0.4 | 34 | 2.1 | 10 | 11 | 58 | 0.5 | 13 | 1.2 | 11.244340 | 3.033150 | 4.442651 | 6.900731 | 11.322691 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 0 |
| 137 | NS | 12.0 | 81218.917413 | 4492.082587 | 50.0 | 7.0 | 0.4 | 0.4 | 34 | 2.1 | 10 | 11 | 58 | 0.5 | 13 | 1.2 | 11.358736 | 5.224940 | 5.817111 | 6.390241 | 11.304903 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 0 |
| 138 | NS | 12.0 | 115004.907303 | 4713.092697 | 50.0 | 7.0 | 0.4 | 0.4 | 34 | 2.1 | 10 | 11 | 58 | 0.5 | 13 | 1.2 | 11.692894 | 4.615192 | 4.143135 | 6.212606 | 11.652730 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 0 |
| 139 | NS | 11.0 | 68550.998470 | 11863.001530 | 50.0 | 7.0 | 0.4 | 0.4 | 34 | 2.1 | 10 | 11 | 58 | 0.5 | 13 | 1.2 | 11.294944 | 1.949359 | 4.174387 | 5.117994 | 11.135333 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 0 |
| 140 | NS | 15.0 | 148509.417153 | 8590.582847 | 50.0 | 7.0 | 0.4 | 0.4 | 34 | 2.1 | 10 | 11 | 58 | 0.5 | 13 | 1.2 | 11.964638 | 4.795832 | 4.718499 | 6.265301 | 11.908404 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 0 |
| 141 | NS | 14.0 | 125274.322772 | 12861.677228 | 50.0 | 7.0 | 0.4 | 0.4 | 34 | 2.1 | 10 | 11 | 58 | 0.5 | 13 | 1.2 | 11.835994 | 5.069517 | 4.718499 | 6.827629 | 11.738261 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 0 |
| 142 | NS | 12.0 | 81218.917413 | -2456.917413 | 50.0 | 7.0 | 0.4 | 0.4 | 34 | 2.1 | 10 | 11 | 58 | 0.5 | 13 | 1.2 | 11.274186 | 2.345208 | 5.817111 | 6.390241 | 11.304903 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 0 |
| 143 | NS | 13.0 | 105405.229084 | 8498.770916 | 50.0 | 7.0 | 0.4 | 0.4 | 34 | 2.1 | 10 | 11 | 58 | 0.5 | 13 | 1.2 | 11.643111 | 3.591657 | 2.564949 | 7.095064 | 11.565568 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 0 |
| 144 | NS | 13.0 | 105101.899758 | 5607.100242 | 50.0 | 7.0 | 0.4 | 0.4 | 34 | 2.1 | 10 | 11 | 58 | 0.5 | 13 | 1.2 | 11.614660 | 5.059644 | 6.452049 | 5.877736 | 11.562686 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 0 |
| 145 | NS | 14.0 | 119824.704879 | 7687.295121 | 50.0 | 7.0 | 0.4 | 0.4 | 34 | 2.1 | 10 | 11 | 58 | 0.5 | 13 | 1.2 | 11.755966 | 5.899152 | 5.817111 | 7.095064 | 11.693785 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 0 |
| 146 | NS | 14.0 | 119824.704879 | -6277.704879 | 50.0 | 7.0 | 0.4 | 0.4 | 34 | 2.1 | 10 | 11 | 58 | 0.5 | 13 | 1.2 | 11.639972 | 4.626013 | 5.817111 | 6.900731 | 11.693785 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 0 |
| 147 | NS | 14.0 | 184294.595000 | 4627.405000 | 50.0 | 7.0 | 0.4 | 0.4 | 34 | 2.1 | 10 | 11 | 58 | 0.5 | 13 | 1.2 | 12.149090 | 2.756810 | 5.666427 | 6.368187 | 12.124291 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 1 | 0 |
| 148 | NS | 13.0 | 98471.000502 | 13346.999498 | 50.0 | 7.0 | 0.4 | 0.4 | 34 | 2.1 | 10 | 11 | 58 | 0.5 | 13 | 1.2 | 11.624628 | 3.240370 | 5.817111 | 5.117994 | 11.497517 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 0 |
| 149 | NS | 13.0 | 99078.000000 | 2846.000000 | 50.0 | 7.0 | 0.4 | 0.4 | 34 | 2.1 | 10 | 11 | 58 | 0.5 | 13 | 1.2 | 11.531983 | 5.504544 | 3.526361 | 6.152733 | 11.503663 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 0 |
| 150 | NS | 13.0 | 96917.464286 | -6011.464286 | 50.0 | 7.0 | 0.4 | 0.4 | 34 | 2.1 | 10 | 11 | 58 | 0.5 | 13 | 1.2 | 11.417581 | 2.302173 | 4.406719 | 6.900731 | 11.481615 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 0 |
| 151 | NS | 13.0 | 100905.286351 | -4986.286351 | 50.0 | 7.0 | 0.4 | 0.4 | 34 | 2.1 | 10 | 11 | 58 | 0.5 | 13 | 1.2 | 11.471259 | 3.860052 | 4.158883 | 7.095064 | 11.521938 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 0 |
| 152 | NS | 13.0 | 104499.288690 | 2934.711310 | 50.0 | 7.0 | 0.4 | 0.4 | 34 | 2.1 | 10 | 11 | 58 | 0.5 | 13 | 1.2 | 11.584632 | 4.837355 | 4.174387 | 6.716595 | 11.556936 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 0 |
| 153 | NS | 12.0 | 82676.532609 | -2014.532609 | 50.0 | 7.0 | 0.4 | 0.4 | 34 | 2.1 | 10 | 11 | 58 | 0.5 | 13 | 1.2 | 11.298023 | 2.024846 | 4.442651 | 7.021976 | 11.322691 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 0 |
| 154 | NS | 15.0 | 148509.417153 | -23514.417153 | 50.0 | 7.0 | 0.4 | 0.4 | 34 | 2.1 | 10 | 11 | 58 | 0.5 | 13 | 1.2 | 11.736029 | 2.323790 | 4.718499 | 7.095064 | 11.908404 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 0 |
| 155 | NS | 11.0 | 68648.288490 | 6727.711510 | 55.0 | 2.0 | 0.4 | 0.4 | 34 | 2.1 | 10 | 11 | 58 | 0.5 | 13 | 1.2 | 11.230244 | 5.735852 | 2.302585 | 6.900731 | 11.136751 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 0 |
| 156 | NS | 12.0 | 81945.397368 | 8398.602632 | 55.0 | 2.0 | 0.4 | 0.4 | 34 | 2.1 | 10 | 11 | 58 | 0.5 | 13 | 1.2 | 11.411380 | 4.795832 | 2.944439 | 6.240276 | 11.313808 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 0 |
| 157 | NS | 12.0 | 86552.911802 | 144.088198 | 55.0 | 2.0 | 0.4 | 0.4 | 34 | 2.1 | 10 | 11 | 58 | 0.5 | 13 | 1.2 | 11.370175 | 6.156298 | 4.174387 | 5.793014 | 11.368511 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 0 |
| 158 | NS | 15.0 | 145985.605941 | 11114.394059 | 55.0 | 2.0 | 0.4 | 0.4 | 34 | 2.1 | 10 | 11 | 58 | 0.5 | 13 | 1.2 | 11.964638 | 6.148170 | 4.317488 | 7.095064 | 11.891263 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 0 |
| 159 | NS | 12.0 | 85002.685484 | 6329.314516 | 55.0 | 2.0 | 0.4 | 0.4 | 34 | 2.1 | 10 | 11 | 58 | 0.5 | 13 | 1.2 | 11.422256 | 5.761944 | 4.418841 | 6.075346 | 11.350438 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 0 |
| 160 | NS | 13.0 | 104574.826233 | 15238.173767 | 55.0 | 2.0 | 0.4 | 0.4 | 34 | 2.1 | 10 | 11 | 58 | 0.5 | 13 | 1.2 | 11.693687 | 5.744563 | 4.418841 | 6.827629 | 11.557658 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 0 |
| 161 | NS | 14.0 | 119824.704879 | -6478.704879 | 55.0 | 2.0 | 0.4 | 0.4 | 34 | 2.1 | 10 | 11 | 58 | 0.5 | 13 | 1.2 | 11.638200 | 3.361547 | 5.817111 | 7.095064 | 11.693785 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 0 |
| 162 | NS | 15.0 | 143028.000000 | 11132.000000 | 55.0 | 2.0 | 0.4 | 0.4 | 34 | 2.1 | 10 | 11 | 58 | 0.5 | 13 | 1.2 | 11.945746 | 6.090977 | 2.484907 | 6.827629 | 11.870796 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 0 |
| 163 | NS | 12.0 | 85478.526232 | 18734.473768 | 55.0 | 2.0 | 0.4 | 0.4 | 34 | 2.1 | 10 | 11 | 58 | 0.5 | 13 | 1.2 | 11.554192 | 3.162278 | 3.912023 | 7.563201 | 11.356020 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 0 |
| 164 | NS | 12.0 | 82676.532609 | 11727.467391 | 55.0 | 2.0 | 0.4 | 0.4 | 34 | 2.1 | 10 | 11 | 58 | 0.5 | 13 | 1.2 | 11.455339 | 5.648008 | 4.442651 | 6.075346 | 11.322691 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 0 |
| 165 | NS | 12.0 | 82168.243394 | 7099.756606 | 55.0 | 2.0 | 0.4 | 0.4 | 34 | 2.1 | 10 | 11 | 58 | 0.5 | 13 | 1.2 | 11.399398 | 5.366563 | 4.317488 | 7.563201 | 11.316524 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 0 |
| 166 | NS | 12.0 | 83656.459986 | 11815.540014 | 55.0 | 2.0 | 0.4 | 0.4 | 34 | 2.1 | 10 | 11 | 58 | 0.5 | 13 | 1.2 | 11.466588 | 4.472136 | 4.025352 | 7.563201 | 11.334474 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 0 |
| 167 | NS | 15.0 | 156835.750000 | 264.250000 | 55.0 | 2.0 | 0.4 | 0.4 | 34 | 2.1 | 10 | 11 | 58 | 0.5 | 13 | 1.2 | 11.964638 | 6.099180 | 3.332205 | 6.827629 | 11.962954 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 0 |
| 168 | NS | 15.0 | 147178.315126 | 6981.684874 | 55.0 | 2.0 | 0.4 | 0.4 | 34 | 2.1 | 10 | 11 | 58 | 0.5 | 13 | 1.2 | 11.945746 | 5.822371 | 4.442651 | 7.095064 | 11.899400 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 0 |
| 169 | NS | 13.0 | 104704.453110 | -25.453110 | 55.0 | 2.0 | 0.4 | 0.4 | 34 | 2.1 | 10 | 11 | 58 | 0.5 | 13 | 1.2 | 11.558654 | 5.805170 | 4.025352 | 5.921578 | 11.558897 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 0 |
| 170 | NS | 11.0 | 68174.382353 | -4394.382353 | 55.0 | 2.0 | 0.4 | 0.4 | 34 | 2.1 | 10 | 11 | 58 | 0.5 | 13 | 1.2 | 11.063195 | 3.577709 | 3.737670 | 4.927254 | 11.129824 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 0 |
| 171 | NS | 12.0 | 82168.243394 | 14437.756606 | 55.0 | 2.0 | 0.4 | 0.4 | 34 | 2.1 | 10 | 11 | 58 | 0.5 | 13 | 1.2 | 11.478396 | 5.567764 | 4.317488 | 7.563201 | 11.316524 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 0 |
| 172 | NS | 12.0 | 83440.561069 | 8763.438931 | 55.0 | 2.0 | 0.4 | 0.4 | 34 | 2.1 | 10 | 11 | 58 | 0.5 | 13 | 1.2 | 11.431759 | 5.683309 | 1.945910 | 5.337538 | 11.331890 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 0 |
| 173 | NS | 14.0 | 123955.919861 | -3238.919861 | 55.0 | 2.0 | 0.4 | 0.4 | 34 | 2.1 | 10 | 11 | 58 | 0.5 | 13 | 1.2 | 11.701204 | 6.099180 | 3.332205 | 5.365976 | 11.727681 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 0 |
| 174 | NS | 13.0 | 104574.826233 | 22251.173767 | 55.0 | 2.0 | 0.4 | 0.4 | 34 | 2.1 | 10 | 11 | 58 | 0.5 | 13 | 1.2 | 11.750571 | 5.761944 | 4.418841 | 6.827629 | 11.557658 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 0 |
| 175 | NS | 13.0 | 100018.809861 | 7415.190139 | 55.0 | 2.0 | 0.4 | 0.4 | 34 | 2.1 | 10 | 11 | 58 | 0.5 | 13 | 1.2 | 11.584632 | 4.472136 | 4.369448 | 5.758902 | 11.513114 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 0 |
| 176 | NS | 15.0 | 144803.680628 | 1023.319372 | 55.0 | 2.0 | 0.4 | 0.4 | 34 | 2.1 | 10 | 11 | 58 | 0.5 | 13 | 1.2 | 11.890176 | 2.949576 | 4.369448 | 6.827629 | 11.883134 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 0 |
| 177 | NS | 13.0 | 104301.705607 | 10331.294393 | 55.0 | 2.0 | 0.4 | 0.4 | 34 | 2.1 | 10 | 11 | 58 | 0.5 | 13 | 1.2 | 11.649491 | 5.567764 | 4.718499 | 6.716595 | 11.555043 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 0 |
| 178 | NS | 11.0 | 65898.205859 | 3115.794141 | 55.0 | 2.0 | 0.4 | 0.4 | 34 | 2.1 | 10 | 11 | 58 | 0.5 | 13 | 1.2 | 11.142065 | 6.024948 | 5.817111 | 7.021976 | 11.095866 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 0 |
| 179 | NS | 12.0 | 81218.917413 | 6745.082587 | 55.0 | 2.0 | 0.4 | 0.4 | 34 | 2.1 | 10 | 11 | 58 | 0.5 | 13 | 1.2 | 11.384683 | 5.882176 | 5.817111 | 5.793014 | 11.304903 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 0 |
| 180 | NS | 13.0 | 100441.761115 | 16459.238885 | 55.0 | 2.0 | 0.4 | 0.4 | 34 | 2.1 | 10 | 11 | 58 | 0.5 | 13 | 1.2 | 11.669083 | 6.041523 | 4.317488 | 7.095064 | 11.517333 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 0 |
| 181 | NS | 11.0 | 68550.998470 | 2435.001530 | 55.0 | 2.0 | 0.4 | 0.4 | 34 | 2.1 | 10 | 11 | 58 | 0.5 | 13 | 1.2 | 11.170238 | 4.219005 | 4.174387 | 7.021976 | 11.135333 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 0 |
| 182 | NS | 12.0 | 84347.371630 | 10796.628370 | 55.0 | 2.0 | 0.4 | 0.4 | 34 | 2.1 | 10 | 11 | 58 | 0.5 | 13 | 1.2 | 11.463147 | 4.753946 | 1.609438 | 5.062595 | 11.342699 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 0 |
| 183 | NS | 13.0 | 98471.000502 | 1448.999498 | 55.0 | 2.0 | 0.4 | 0.4 | 34 | 2.1 | 10 | 11 | 58 | 0.5 | 13 | 1.2 | 11.512125 | 3.687818 | 5.817111 | 6.152733 | 11.497517 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 0 |
| 184 | NS | 12.0 | 81218.917413 | -3526.917413 | 55.0 | 2.0 | 0.4 | 0.4 | 34 | 2.1 | 10 | 11 | 58 | 0.5 | 13 | 1.2 | 11.260508 | 1.949359 | 5.817111 | 5.476464 | 11.304903 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 0 |
| 185 | NS | 11.0 | 68045.742806 | -8130.742806 | 55.0 | 2.0 | 0.4 | 0.4 | 34 | 2.1 | 10 | 11 | 58 | 0.5 | 13 | 1.2 | 11.000682 | 3.974921 | 4.406719 | 6.212606 | 11.127935 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 0 |
| 186 | NS | 15.0 | 148432.131584 | 8667.868416 | 55.0 | 2.0 | 0.4 | 0.4 | 34 | 2.1 | 10 | 11 | 58 | 0.5 | 13 | 1.2 | 11.964638 | 3.885872 | 5.176150 | 7.095064 | 11.907883 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 0 |
| 187 | NS | 12.0 | 82168.243394 | 1226.756606 | 55.0 | 2.0 | 0.4 | 0.4 | 34 | 2.1 | 10 | 11 | 58 | 0.5 | 13 | 1.2 | 11.331344 | 5.683309 | 4.317488 | 5.384495 | 11.316524 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 0 |
| 188 | NS | 11.0 | 68807.640150 | 7936.359850 | 55.0 | 2.0 | 0.4 | 0.4 | 34 | 2.1 | 10 | 11 | 58 | 0.5 | 13 | 1.2 | 11.248230 | 5.431390 | 4.158883 | 6.075346 | 11.139070 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 0 |
| 189 | NS | 13.0 | 98471.000502 | -4811.000502 | 55.0 | 2.0 | 0.4 | 0.4 | 34 | 2.1 | 10 | 11 | 58 | 0.5 | 13 | 1.2 | 11.447426 | 5.477226 | 5.817111 | 5.620401 | 11.497517 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 0 |
| 190 | NS | 15.0 | 144848.020486 | 10380.979514 | 55.0 | 2.0 | 0.4 | 0.4 | 34 | 2.1 | 10 | 11 | 58 | 0.5 | 13 | 1.2 | 11.952657 | 6.082763 | 5.817111 | 6.152733 | 11.883440 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 0 |
| 191 | NS | 12.0 | 91743.951531 | -4489.951531 | 55.0 | 2.0 | 0.4 | 0.4 | 34 | 2.1 | 10 | 11 | 58 | 0.5 | 13 | 1.2 | 11.376579 | 2.898275 | 6.452049 | 6.698268 | 11.426757 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 0 |
| 192 | NS | 13.0 | 104850.691877 | 12050.308123 | 55.0 | 2.0 | 0.4 | 0.4 | 34 | 2.1 | 10 | 11 | 58 | 0.5 | 13 | 1.2 | 11.669083 | 5.486347 | 2.302585 | 6.827629 | 11.560293 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 0 |
| 193 | NS | 14.0 | 125274.322772 | 20465.677228 | 55.0 | 2.0 | 0.4 | 0.4 | 34 | 2.1 | 10 | 11 | 58 | 0.5 | 13 | 1.2 | 11.889579 | 5.621388 | 4.718499 | 7.021976 | 11.738261 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 0 |
| 194 | NS | 10.0 | 69424.354545 | -816.354545 | 55.0 | 2.0 | 0.4 | 0.4 | 34 | 2.1 | 10 | 11 | 58 | 0.5 | 13 | 1.2 | 11.136164 | 3.464102 | 4.454347 | 6.368187 | 11.147993 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 0 |
| 195 | NS | 15.0 | 148432.131584 | 15642.868416 | 55.0 | 2.0 | 0.4 | 0.4 | 34 | 2.1 | 10 | 11 | 58 | 0.5 | 13 | 1.2 | 12.008079 | 5.215362 | 5.176150 | 7.021976 | 11.907883 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 0 |
| 196 | NS | 15.0 | 148432.131584 | -2580.131584 | 55.0 | 2.0 | 0.4 | 0.4 | 34 | 2.1 | 10 | 11 | 58 | 0.5 | 13 | 1.2 | 11.890348 | 5.477226 | 5.176150 | 6.390241 | 11.907883 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 0 |
| 197 | NS | 13.0 | 105101.899758 | 6705.100242 | 55.0 | 2.0 | 0.4 | 0.4 | 34 | 2.1 | 10 | 11 | 58 | 0.5 | 13 | 1.2 | 11.624529 | 2.345208 | 6.452049 | 7.021976 | 11.562686 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 0 |
| 198 | NS | 12.0 | 84785.351852 | 5558.648148 | 55.0 | 2.0 | 0.4 | 0.4 | 34 | 2.1 | 10 | 11 | 58 | 0.5 | 13 | 1.2 | 11.411380 | 5.949790 | 2.302585 | 5.278115 | 11.347878 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 0 |
| 199 | NS | 12.0 | 115004.907303 | 12829.092697 | 55.0 | 2.0 | 0.4 | 0.4 | 34 | 2.1 | 10 | 11 | 58 | 0.5 | 13 | 1.2 | 11.758488 | 5.692100 | 4.143135 | 7.021976 | 11.652730 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 0 |
| 200 | NS | 15.0 | 148517.790123 | -643.790123 | 55.0 | 2.0 | 0.4 | 0.4 | 34 | 2.1 | 10 | 11 | 58 | 0.5 | 13 | 1.2 | 11.904116 | 6.090977 | 1.386294 | 5.476464 | 11.908460 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 0 |
| 201 | NS | 15.0 | 145985.605941 | -8446.605941 | 55.0 | 2.0 | 0.4 | 0.4 | 34 | 2.1 | 10 | 11 | 58 | 0.5 | 13 | 1.2 | 11.831663 | 5.540758 | 4.317488 | 5.793014 | 11.891263 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 0 |
| 202 | NS | 12.0 | 86552.911802 | -841.911802 | 55.0 | 2.0 | 0.4 | 0.4 | 34 | 2.1 | 10 | 11 | 58 | 0.5 | 13 | 1.2 | 11.358736 | 3.820995 | 4.174387 | 6.698268 | 11.368511 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 0 |
| 203 | NS | 14.0 | 125308.250599 | 6673.749401 | 55.0 | 2.0 | 0.4 | 0.4 | 34 | 2.1 | 10 | 11 | 58 | 0.5 | 13 | 1.2 | 11.790421 | 5.856620 | 4.025352 | 5.758902 | 11.738532 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 0 |
| 204 | NS | 14.0 | 119824.704879 | 7687.295121 | 55.0 | 2.0 | 0.4 | 0.4 | 34 | 2.1 | 10 | 11 | 58 | 0.5 | 13 | 1.2 | 11.755966 | 5.692100 | 5.817111 | 6.827629 | 11.693785 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 0 |
| 205 | NS | 14.0 | 122479.704518 | -6243.704518 | 55.0 | 2.0 | 0.4 | 0.4 | 34 | 2.1 | 10 | 11 | 58 | 0.5 | 13 | 1.2 | 11.663378 | 4.049691 | 5.176150 | 7.021976 | 11.715701 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 0 |
| 206 | NS | 15.0 | 148118.417969 | 8981.582031 | 55.0 | 2.0 | 0.4 | 0.4 | 34 | 2.1 | 10 | 11 | 58 | 0.5 | 13 | 1.2 | 11.964638 | 3.449638 | 4.174387 | 7.021976 | 11.905767 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 0 |
| 207 | NS | 14.0 | 123955.919861 | 3556.080139 | 55.0 | 2.0 | 0.4 | 0.4 | 34 | 2.1 | 10 | 11 | 58 | 0.5 | 13 | 1.2 | 11.755966 | 5.440588 | 3.332205 | 6.827629 | 11.727681 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 0 |
| 208 | NS | 12.0 | 83989.772593 | -7543.772593 | 55.0 | 2.0 | 0.4 | 0.4 | 34 | 2.1 | 10 | 11 | 58 | 0.5 | 13 | 1.2 | 11.244340 | 4.037326 | 4.770685 | 5.620401 | 11.338450 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 0 |
| 209 | NS | 13.0 | 102166.799898 | -2996.799898 | 55.0 | 2.0 | 0.4 | 0.4 | 34 | 2.1 | 10 | 11 | 58 | 0.5 | 13 | 1.2 | 11.504591 | 5.753260 | 4.442651 | 5.746203 | 11.534362 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 0 |
| 210 | NS | 11.0 | 68807.640150 | 6568.359850 | 55.0 | 2.0 | 0.4 | 0.4 | 34 | 2.1 | 10 | 11 | 58 | 0.5 | 13 | 1.2 | 11.230244 | 5.431390 | 4.158883 | 5.030438 | 11.139070 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 0 |
| 211 | NS | 14.0 | 122526.088857 | 15609.911143 | 55.0 | 2.0 | 0.4 | 0.4 | 34 | 2.1 | 10 | 11 | 58 | 0.5 | 13 | 1.2 | 11.835994 | 5.761944 | 4.317488 | 7.095064 | 11.716079 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 0 |
| 212 | NS | 15.0 | 148758.813285 | 5401.186715 | 55.0 | 2.0 | 0.4 | 0.4 | 34 | 2.1 | 10 | 11 | 58 | 0.5 | 13 | 1.2 | 11.945746 | 5.567764 | 4.418841 | 7.095064 | 11.910082 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 0 |
| 213 | NS | 14.0 | 184294.595000 | 8564.405000 | 55.0 | 2.0 | 0.4 | 0.4 | 34 | 2.1 | 10 | 11 | 58 | 0.5 | 13 | 1.2 | 12.169715 | 3.987480 | 5.666427 | 6.075346 | 12.124291 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 1 | 0 |
| 214 | NS | 13.0 | 100441.761115 | 26745.238885 | 55.0 | 2.0 | 0.4 | 0.4 | 34 | 2.1 | 10 | 11 | 58 | 0.5 | 13 | 1.2 | 11.753414 | 5.830952 | 4.317488 | 7.563201 | 11.517333 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 0 |
| 215 | NS | 14.0 | 125308.250599 | 6673.749401 | 55.0 | 2.0 | 0.4 | 0.4 | 34 | 2.1 | 10 | 11 | 58 | 0.5 | 13 | 1.2 | 11.790421 | 5.916080 | 4.025352 | 5.758902 | 11.738532 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 0 |
| 216 | NS | 12.0 | 83314.435543 | 7029.564457 | 55.0 | 2.0 | 0.4 | 0.4 | 34 | 2.1 | 10 | 11 | 58 | 0.5 | 13 | 1.2 | 11.411380 | 5.949790 | 2.564949 | 5.420535 | 11.330377 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 0 |
| 217 | NS | 15.0 | 148432.131584 | 8667.868416 | 55.0 | 2.0 | 0.4 | 0.4 | 34 | 2.1 | 10 | 11 | 58 | 0.5 | 13 | 1.2 | 11.964638 | 5.899152 | 5.176150 | 7.021976 | 11.907883 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 0 |
| 218 | NS | 12.0 | 81888.167064 | 8455.832936 | 55.0 | 2.0 | 0.4 | 0.4 | 34 | 2.1 | 10 | 11 | 58 | 0.5 | 13 | 1.2 | 11.411380 | 5.449771 | 3.332205 | 4.890349 | 11.313110 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 0 |
| 219 | NS | 15.0 | 147178.315126 | 16227.684874 | 55.0 | 2.0 | 0.4 | 0.4 | 34 | 2.1 | 10 | 11 | 58 | 0.5 | 13 | 1.2 | 12.003993 | 5.822371 | 4.442651 | 7.021976 | 11.899400 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 0 |
| 220 | NS | 14.0 | 122479.704518 | 8268.295482 | 55.0 | 2.0 | 0.4 | 0.4 | 34 | 2.1 | 10 | 11 | 58 | 0.5 | 13 | 1.2 | 11.781027 | 4.494441 | 5.176150 | 6.900731 | 11.715701 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 0 |
| 221 | NS | 12.0 | 91743.951531 | -7898.951531 | 60.0 | -3.0 | 0.4 | 0.4 | 34 | 2.1 | 10 | 11 | 58 | 0.5 | 13 | 1.2 | 11.336725 | 5.549775 | 6.452049 | 7.021976 | 11.426757 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 0 |
| 222 | NS | 11.0 | 68157.694737 | 3757.305263 | 60.0 | -3.0 | 0.4 | 0.4 | 34 | 2.1 | 10 | 11 | 58 | 0.5 | 13 | 1.2 | 11.183240 | 3.577709 | 4.025352 | 6.265301 | 11.129579 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 0 |
| 223 | NS | 15.0 | 214400.347826 | 20599.652174 | 60.0 | -3.0 | 0.4 | 0.4 | 34 | 2.1 | 10 | 11 | 58 | 0.5 | 13 | 1.2 | 12.367341 | 2.345208 | 5.666427 | 7.095064 | 12.275600 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 1 | 0 |
| 224 | NS | 13.0 | 104253.048969 | -5083.048969 | 60.0 | -3.0 | 0.4 | 0.4 | 34 | 2.1 | 10 | 11 | 58 | 0.5 | 13 | 1.2 | 11.504591 | 2.073644 | 2.890372 | 6.900731 | 11.554576 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 1 |
| 225 | NS | 15.0 | 148432.131584 | 8667.868416 | 60.0 | -3.0 | 0.4 | 0.4 | 34 | 2.1 | 10 | 11 | 58 | 0.5 | 13 | 1.2 | 11.964638 | 5.009990 | 5.176150 | 7.095064 | 11.907883 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 0 |
| 226 | NS | 13.0 | 102868.810458 | 4565.189542 | 60.0 | -3.0 | 0.4 | 0.4 | 34 | 2.1 | 10 | 11 | 58 | 0.5 | 13 | 1.2 | 11.584632 | 6.212890 | 2.397895 | 5.164786 | 11.541210 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 0 |
| 227 | NS | 12.0 | 94618.853659 | 7763.146341 | 60.0 | -3.0 | 0.4 | 0.4 | 34 | 2.1 | 10 | 11 | 58 | 0.5 | 13 | 1.2 | 11.536466 | 4.000000 | 1.945910 | 6.716595 | 11.457612 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 0 |
| 228 | NS | 12.0 | 87127.450000 | 1096.550000 | 60.0 | -3.0 | 0.4 | 0.4 | 34 | 2.1 | 10 | 11 | 58 | 0.5 | 13 | 1.2 | 11.387634 | 5.458938 | 1.386294 | 7.021976 | 11.375127 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 0 |
| 229 | NS | 12.0 | 91743.951531 | -5711.951531 | 60.0 | -3.0 | 0.4 | 0.4 | 34 | 2.1 | 10 | 11 | 58 | 0.5 | 13 | 1.2 | 11.362475 | 3.701351 | 6.452049 | 7.021976 | 11.426757 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 0 |
| 230 | NS | 15.0 | 144848.020486 | 5144.979514 | 60.0 | -3.0 | 0.4 | 0.4 | 34 | 2.1 | 10 | 11 | 58 | 0.5 | 13 | 1.2 | 11.918344 | 3.820995 | 5.817111 | 7.095064 | 11.883440 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 0 |
| 231 | NS | 13.0 | 102568.856287 | -8908.856287 | 60.0 | -3.0 | 0.4 | 0.4 | 34 | 2.1 | 10 | 11 | 58 | 0.5 | 13 | 1.2 | 11.447426 | 5.656854 | 3.912023 | 5.384495 | 11.538290 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 0 |
| 232 | NS | 12.0 | 84755.875000 | 3482.125000 | 60.0 | -3.0 | 0.4 | 0.4 | 34 | 2.1 | 10 | 11 | 58 | 0.5 | 13 | 1.2 | 11.387793 | 5.890671 | 1.098612 | 6.390241 | 11.347530 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 0 |
| 233 | NS | 13.0 | 104574.826233 | -2650.826233 | 60.0 | -3.0 | 0.4 | 0.4 | 34 | 2.1 | 10 | 11 | 58 | 0.5 | 13 | 1.2 | 11.531983 | 4.289522 | 4.418841 | 6.698268 | 11.557658 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 0 |
| 234 | NS | 12.0 | 87504.272358 | 2839.727642 | 60.0 | -3.0 | 0.4 | 0.4 | 34 | 2.1 | 10 | 11 | 58 | 0.5 | 13 | 1.2 | 11.411380 | 6.473021 | 2.890372 | 5.476464 | 11.379443 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 0 |
| 235 | NS | 15.0 | 147448.118189 | 9651.881811 | 60.0 | -3.0 | 0.4 | 0.4 | 34 | 2.1 | 10 | 11 | 58 | 0.5 | 13 | 1.2 | 11.964638 | 6.480741 | 2.944439 | 6.900731 | 11.901232 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 0 |
| 236 | NS | 12.0 | 91743.951531 | -3519.951531 | 60.0 | -3.0 | 0.4 | 0.4 | 34 | 2.1 | 10 | 11 | 58 | 0.5 | 13 | 1.2 | 11.387634 | 2.049390 | 6.452049 | 7.095064 | 11.426757 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 0 |
| 237 | NS | 14.0 | 122511.090909 | 1027.909091 | 60.0 | -3.0 | 0.4 | 0.4 | 34 | 2.1 | 10 | 11 | 58 | 0.5 | 13 | 1.2 | 11.724312 | 5.138093 | 4.770685 | 6.745236 | 11.715957 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 0 |
| 238 | NS | 11.0 | 68045.742806 | -2332.742806 | 60.0 | -3.0 | 0.4 | 0.4 | 34 | 2.1 | 10 | 11 | 58 | 0.5 | 13 | 1.2 | 11.093052 | 4.171331 | 4.406719 | 4.927254 | 11.127935 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 0 |
| 239 | NS | 11.0 | 66556.592744 | 15462.407256 | 60.0 | -3.0 | 0.4 | 0.4 | 34 | 2.1 | 10 | 11 | 58 | 0.5 | 13 | 1.2 | 11.314706 | 6.403124 | 4.317488 | 7.095064 | 11.105808 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 0 |
| 240 | NS | 12.0 | 87398.566265 | 15156.433735 | 60.0 | -3.0 | 0.4 | 0.4 | 34 | 2.1 | 10 | 11 | 58 | 0.5 | 13 | 1.2 | 11.538155 | 3.619392 | 3.637586 | 7.021976 | 11.378234 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 0 |
| 241 | NS | 13.0 | 104704.453110 | 6982.546890 | 60.0 | -3.0 | 0.4 | 0.4 | 34 | 2.1 | 10 | 11 | 58 | 0.5 | 13 | 1.2 | 11.623456 | 6.379655 | 4.025352 | 5.793014 | 11.558897 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 0 |
| 242 | NS | 15.0 | 145298.376900 | 11801.623100 | 60.0 | -3.0 | 0.4 | 0.4 | 34 | 2.1 | 10 | 11 | 58 | 0.5 | 13 | 1.2 | 11.964638 | 6.348228 | 4.770685 | 7.095064 | 11.886545 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 0 |
| 243 | NS | 13.0 | 104574.826233 | 2011.173767 | 60.0 | -3.0 | 0.4 | 0.4 | 34 | 2.1 | 10 | 11 | 58 | 0.5 | 13 | 1.2 | 11.576707 | 4.277850 | 4.418841 | 5.793014 | 11.557658 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 0 |
| 244 | NS | 14.0 | 125706.569216 | 1242.430784 | 60.0 | -3.0 | 0.4 | 0.4 | 34 | 2.1 | 10 | 11 | 58 | 0.5 | 13 | 1.2 | 11.751541 | 5.549775 | 3.737670 | 6.716595 | 11.741706 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 0 |
| 245 | NS | 12.0 | 81270.257040 | 9073.742960 | 60.0 | -3.0 | 0.4 | 0.4 | 34 | 2.1 | 10 | 11 | 58 | 0.5 | 13 | 1.2 | 11.411380 | 5.029911 | 4.406719 | 4.477337 | 11.305535 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 0 |
| 246 | NS | 13.0 | 105127.753680 | 2306.246320 | 60.0 | -3.0 | 0.4 | 0.4 | 34 | 2.1 | 10 | 11 | 58 | 0.5 | 13 | 1.2 | 11.584632 | 5.735852 | 3.737670 | 5.062595 | 11.562932 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 0 |
| 247 | NS | 9.0 | 59884.373783 | 6974.626217 | 60.0 | -3.0 | 0.4 | 0.4 | 34 | 2.1 | 10 | 11 | 58 | 0.5 | 13 | 1.2 | 11.110341 | 2.469818 | 3.912023 | 6.698268 | 11.000171 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 0 |
| 248 | NS | 11.0 | 66409.794212 | -696.794212 | 60.0 | -3.0 | 0.4 | 0.4 | 34 | 2.1 | 10 | 11 | 58 | 0.5 | 13 | 1.2 | 11.093052 | 1.673320 | 4.369448 | 6.075346 | 11.103600 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 0 |
| 249 | NS | 12.0 | 85478.526232 | -6716.526232 | 60.0 | -3.0 | 0.4 | 0.4 | 34 | 2.1 | 10 | 11 | 58 | 0.5 | 13 | 1.2 | 11.274186 | 3.949684 | 3.912023 | 5.384495 | 11.356020 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 0 |
| ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... |
| 14670 | SC | 15.0 | 149430.750000 | -2142.750000 | 40.0 | 17.0 | 0.4 | 0.5 | 38 | 1.9 | 12 | 14 | 55 | 0.4 | 12 | 1.4 | 11.900145 | 1.949359 | 1.609438 | 7.172425 | 11.914588 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 0 |
| 14671 | SC | 11.0 | 79100.263771 | 8559.736229 | 40.0 | 17.0 | 0.4 | 0.5 | 38 | 1.9 | 12 | 14 | 55 | 0.4 | 12 | 1.4 | 11.381221 | 1.673320 | 6.363028 | 5.493061 | 11.278471 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 0 |
| 14672 | SC | 9.0 | 60393.678745 | -11990.678745 | 40.0 | 17.0 | 0.4 | 0.5 | 38 | 1.9 | 12 | 14 | 55 | 0.4 | 12 | 1.4 | 10.787317 | 1.000000 | 3.850148 | 6.593045 | 11.008640 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 0 |
| 14673 | SC | 12.0 | 85066.240725 | -5512.240725 | 40.0 | 17.0 | 0.4 | 0.5 | 38 | 1.9 | 12 | 14 | 55 | 0.4 | 12 | 1.4 | 11.284191 | 0.632456 | 2.772589 | 5.811141 | 11.351186 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 0 |
| 14674 | SC | 15.0 | 220806.750000 | 10505.250000 | 40.0 | 17.0 | 0.4 | 0.5 | 38 | 1.9 | 12 | 14 | 55 | 0.4 | 12 | 1.4 | 12.351523 | 1.732051 | 5.525453 | 6.364751 | 12.305043 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 1 | 0 |
| 14675 | SC | 9.0 | 75003.755877 | 5465.244123 | 40.0 | 17.0 | 0.4 | 0.5 | 38 | 1.9 | 12 | 14 | 55 | 0.4 | 12 | 1.4 | 11.295627 | 3.286335 | 3.713572 | 6.663133 | 11.225293 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 0 |
| 14676 | SC | 15.0 | 149705.694175 | 8994.305825 | 40.0 | 17.0 | 0.4 | 0.5 | 38 | 1.9 | 12 | 14 | 55 | 0.4 | 12 | 1.4 | 11.974771 | 2.489980 | 5.521461 | 7.237778 | 11.916427 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 0 |
| 14677 | SC | 11.0 | 70358.898088 | -4511.898088 | 40.0 | 17.0 | 0.4 | 0.5 | 38 | 1.9 | 12 | 14 | 55 | 0.4 | 12 | 1.4 | 11.095089 | 1.303840 | 4.605170 | 6.924612 | 11.161365 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 1 | 0 | 0 | 1 |
| 14678 | SC | 11.0 | 79100.263771 | 10309.736229 | 40.0 | 17.0 | 0.4 | 0.5 | 38 | 1.9 | 12 | 14 | 55 | 0.4 | 12 | 1.4 | 11.400988 | 3.549648 | 6.363028 | 7.161622 | 11.278471 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 0 |
| 14679 | SC | 9.0 | 59510.363429 | 183.636571 | 40.0 | 17.0 | 0.4 | 0.5 | 38 | 1.9 | 12 | 14 | 55 | 0.4 | 12 | 1.4 | 10.996987 | 1.843909 | 4.852030 | 5.438079 | 10.993906 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 1 | 0 | 0 | 1 |
| 14680 | SC | 15.0 | 220806.750000 | 85505.250000 | 40.0 | 17.0 | 0.4 | 0.5 | 38 | 1.9 | 12 | 14 | 55 | 0.4 | 12 | 1.4 | 12.632359 | 1.183216 | 5.525453 | 6.663133 | 12.305043 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 1 | 0 |
| 14681 | SC | 15.0 | 149705.694175 | 8994.305825 | 40.0 | 17.0 | 0.4 | 0.5 | 38 | 1.9 | 12 | 14 | 55 | 0.4 | 12 | 1.4 | 11.974771 | 2.828427 | 5.521461 | 7.237778 | 11.916427 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 0 |
| 14682 | SC | 10.0 | 70840.400546 | 10752.599454 | 40.0 | 17.0 | 0.4 | 0.5 | 38 | 1.9 | 12 | 14 | 55 | 0.4 | 12 | 1.4 | 11.309499 | 2.509980 | 6.363028 | 5.493061 | 11.168185 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 0 |
| 14683 | SC | 9.0 | 59510.363429 | -11107.363429 | 40.0 | 17.0 | 0.4 | 0.5 | 38 | 1.9 | 12 | 14 | 55 | 0.4 | 12 | 1.4 | 10.787317 | 0.632456 | 4.852030 | 7.161622 | 10.993906 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 1 |
| 14684 | SC | 15.0 | 220806.750000 | -8201.750000 | 40.0 | 17.0 | 0.4 | 0.5 | 38 | 1.9 | 12 | 14 | 55 | 0.4 | 12 | 1.4 | 12.267191 | 1.341641 | 5.525453 | 7.161622 | 12.305043 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 1 | 0 |
| 14685 | SC | 13.0 | 101243.170958 | 10745.829042 | 40.0 | 17.0 | 0.4 | 0.5 | 38 | 1.9 | 12 | 14 | 55 | 0.4 | 12 | 1.4 | 11.626156 | 1.183216 | 5.056246 | 7.524561 | 11.525281 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 0 |
| 14686 | SC | 13.0 | 101243.170958 | -6647.170958 | 40.0 | 17.0 | 0.4 | 0.5 | 38 | 1.9 | 12 | 14 | 55 | 0.4 | 12 | 1.4 | 11.457370 | 2.236068 | 5.056246 | 5.252273 | 11.525281 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 0 |
| 14687 | SC | 12.0 | 87754.776289 | 264.223711 | 40.0 | 17.0 | 0.4 | 0.5 | 38 | 1.9 | 12 | 14 | 55 | 0.4 | 12 | 1.4 | 11.385308 | 3.646917 | 3.135494 | 6.364751 | 11.382302 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 0 |
| 14688 | SC | 13.0 | 109912.503580 | 8664.496420 | 40.0 | 17.0 | 0.4 | 0.5 | 38 | 1.9 | 12 | 14 | 55 | 0.4 | 12 | 1.4 | 11.683318 | 3.130495 | 2.890372 | 7.524561 | 11.607440 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 0 |
| 14689 | SC | 15.0 | 149705.694175 | 8994.305825 | 40.0 | 17.0 | 0.4 | 0.5 | 38 | 1.9 | 12 | 14 | 55 | 0.4 | 12 | 1.4 | 11.974771 | 3.860052 | 5.521461 | 7.237778 | 11.916427 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 0 |
| 14690 | SC | 9.0 | 55821.734185 | -1906.734185 | 40.0 | 17.0 | 0.4 | 0.5 | 38 | 1.9 | 12 | 14 | 55 | 0.4 | 12 | 1.4 | 10.895164 | 0.447214 | 4.605170 | 7.524561 | 10.929919 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 1 | 0 | 0 | 1 |
| 14691 | SC | 13.0 | 106116.637459 | -8738.637459 | 40.0 | 17.0 | 0.4 | 0.5 | 38 | 1.9 | 12 | 14 | 55 | 0.4 | 12 | 1.4 | 11.486356 | 1.673320 | 6.363028 | 6.663133 | 11.572294 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 0 |
| 14692 | SC | 13.0 | 105047.296509 | -10451.296509 | 40.0 | 17.0 | 0.4 | 0.5 | 38 | 1.9 | 12 | 14 | 55 | 0.4 | 12 | 1.4 | 11.457370 | 3.240370 | 4.804021 | 5.356586 | 11.562166 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 0 |
| 14693 | SC | 12.0 | 85317.482394 | -6393.482394 | 40.0 | 17.0 | 0.4 | 0.5 | 38 | 1.9 | 12 | 14 | 55 | 0.4 | 12 | 1.4 | 11.276241 | 1.048809 | 4.852030 | 6.924612 | 11.354135 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 1 |
| 14694 | SC | 12.0 | 92736.988124 | 43039.011876 | 40.0 | 17.0 | 0.4 | 0.5 | 38 | 1.9 | 12 | 14 | 55 | 0.4 | 12 | 1.4 | 11.818762 | 1.516575 | 6.363028 | 5.811141 | 11.437523 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 0 |
| 14695 | SC | 9.0 | 53456.085470 | -5053.085470 | 40.0 | 17.0 | 0.4 | 0.5 | 38 | 1.9 | 12 | 14 | 55 | 0.4 | 12 | 1.4 | 10.787317 | 0.836660 | 2.484907 | 5.313206 | 10.886616 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 0 |
| 14696 | SC | 11.0 | 70358.898088 | 3997.101912 | 45.0 | 12.0 | 0.4 | 0.5 | 38 | 1.9 | 12 | 14 | 55 | 0.4 | 12 | 1.4 | 11.216620 | 3.781534 | 4.605170 | 6.603944 | 11.161365 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 0 |
| 14697 | SC | 11.0 | 79991.562780 | -1333.562780 | 45.0 | 12.0 | 0.4 | 0.5 | 38 | 1.9 | 12 | 14 | 55 | 0.4 | 12 | 1.4 | 11.272865 | 2.701851 | 2.995732 | 4.744932 | 11.289676 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 0 |
| 14698 | SC | 11.0 | 79991.562780 | -2113.562780 | 45.0 | 12.0 | 0.4 | 0.5 | 38 | 1.9 | 12 | 14 | 55 | 0.4 | 12 | 1.4 | 11.262899 | 0.948683 | 2.995732 | 4.955827 | 11.289676 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 0 |
| 14699 | SC | 11.0 | 79100.263771 | 9628.736229 | 45.0 | 12.0 | 0.4 | 0.5 | 38 | 1.9 | 12 | 14 | 55 | 0.4 | 12 | 1.4 | 11.393342 | 0.774597 | 6.363028 | 7.524561 | 11.278471 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 0 |
| 14700 | SC | 8.0 | 57091.339212 | 1775.660788 | 45.0 | 12.0 | 0.4 | 0.5 | 38 | 1.9 | 12 | 14 | 55 | 0.4 | 12 | 1.4 | 10.983036 | 2.509980 | 4.852030 | 6.322565 | 10.952408 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 0 |
| 14701 | SC | 15.0 | 220806.750000 | -21174.750000 | 45.0 | 12.0 | 0.4 | 0.5 | 38 | 1.9 | 12 | 14 | 55 | 0.4 | 12 | 1.4 | 12.204231 | 3.687818 | 5.525453 | 6.322565 | 12.305043 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 1 | 0 |
| 14702 | SC | 9.0 | 59510.363429 | 10446.636571 | 45.0 | 12.0 | 0.4 | 0.5 | 38 | 1.9 | 12 | 14 | 55 | 0.4 | 12 | 1.4 | 11.155636 | 2.569047 | 4.852030 | 6.548219 | 10.993906 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 0 |
| 14703 | SC | 11.0 | 69488.327856 | 11604.672144 | 45.0 | 12.0 | 0.4 | 0.5 | 38 | 1.9 | 12 | 14 | 55 | 0.4 | 12 | 1.4 | 11.303352 | 1.224745 | 4.304065 | 6.001415 | 11.148914 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 0 |
| 14704 | SC | 7.0 | 45683.103896 | 2843.896104 | 45.0 | 12.0 | 0.4 | 0.5 | 38 | 1.9 | 12 | 14 | 55 | 0.4 | 12 | 1.4 | 10.789876 | 0.707107 | 4.852030 | 5.609472 | 10.729484 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 0 |
| 14705 | SC | 12.0 | 85699.737850 | -1277.737850 | 45.0 | 12.0 | 0.4 | 0.5 | 38 | 1.9 | 12 | 14 | 55 | 0.4 | 12 | 1.4 | 11.343583 | 4.219005 | 3.850148 | 6.593045 | 11.358605 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 0 |
| 14706 | SC | 15.0 | 149705.694175 | 8994.305825 | 45.0 | 12.0 | 0.4 | 0.5 | 38 | 1.9 | 12 | 14 | 55 | 0.4 | 12 | 1.4 | 11.974771 | 0.894427 | 5.521461 | 7.237778 | 11.916427 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 0 |
| 14707 | SC | 11.0 | 69409.643631 | -4991.643631 | 45.0 | 12.0 | 0.4 | 0.5 | 38 | 1.9 | 12 | 14 | 55 | 0.4 | 12 | 1.4 | 11.073148 | 1.870829 | 3.806662 | 7.161622 | 11.147781 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 0 |
| 14708 | SC | 12.0 | 86959.975696 | -3718.975696 | 45.0 | 12.0 | 0.4 | 0.5 | 38 | 1.9 | 12 | 14 | 55 | 0.4 | 12 | 1.4 | 11.329495 | 1.816590 | 4.127134 | 5.493061 | 11.373203 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 0 |
| 14709 | SC | 9.0 | 53456.085470 | -5053.085470 | 45.0 | 12.0 | 0.4 | 0.5 | 38 | 1.9 | 12 | 14 | 55 | 0.4 | 12 | 1.4 | 10.787317 | 2.569047 | 2.484907 | 5.438079 | 10.886616 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 0 |
| 14710 | SC | 12.0 | 86675.948025 | -16483.948025 | 45.0 | 12.0 | 0.4 | 0.5 | 38 | 1.9 | 12 | 14 | 55 | 0.4 | 12 | 1.4 | 11.158990 | 0.774597 | 2.639057 | 5.442418 | 11.369932 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 0 |
| 14711 | SC | 10.0 | 70840.400546 | 11490.599454 | 45.0 | 12.0 | 0.4 | 0.5 | 38 | 1.9 | 12 | 14 | 55 | 0.4 | 12 | 1.4 | 11.318503 | 2.738613 | 6.363028 | 6.716595 | 11.168185 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 0 |
| 14712 | SC | 13.0 | 101616.197745 | -10751.197745 | 45.0 | 12.0 | 0.4 | 0.5 | 38 | 1.9 | 12 | 14 | 55 | 0.4 | 12 | 1.4 | 11.417130 | 3.768289 | 4.304065 | 5.493061 | 11.528958 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 0 |
| 14713 | SC | 12.0 | 82806.036058 | 6720.963942 | 45.0 | 12.0 | 0.4 | 0.5 | 38 | 1.9 | 12 | 14 | 55 | 0.4 | 12 | 1.4 | 11.402296 | 3.405877 | 3.555348 | 6.001415 | 11.324256 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 0 |
| 14714 | SC | 13.0 | 102036.869565 | -4658.869565 | 45.0 | 12.0 | 0.4 | 0.5 | 38 | 1.9 | 12 | 14 | 55 | 0.4 | 12 | 1.4 | 11.486356 | 1.816590 | 4.077537 | 6.603944 | 11.533089 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 1 | 0 |
| 14715 | SC | 13.0 | 123514.663543 | 777.336457 | 45.0 | 12.0 | 0.4 | 0.5 | 38 | 1.9 | 12 | 14 | 55 | 0.4 | 12 | 1.4 | 11.730389 | 2.000000 | 4.043051 | 4.709530 | 11.724115 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 0 |
| 14716 | SC | 13.0 | 103533.818511 | 4973.181489 | 45.0 | 12.0 | 0.4 | 0.5 | 38 | 1.9 | 12 | 14 | 55 | 0.4 | 12 | 1.4 | 11.594570 | 4.929503 | 3.367296 | 5.590987 | 11.547654 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 0 |
| 14717 | SC | 9.0 | 63564.446718 | 5681.553282 | 45.0 | 12.0 | 0.4 | 0.5 | 38 | 1.9 | 12 | 14 | 55 | 0.4 | 12 | 1.4 | 11.145421 | 1.303840 | 6.363028 | 6.548219 | 11.059810 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 0 |
| 14718 | SC | 11.0 | 68579.699653 | -10017.699653 | 45.0 | 12.0 | 0.4 | 0.5 | 38 | 1.9 | 12 | 14 | 55 | 0.4 | 12 | 1.4 | 10.977841 | 0.316228 | 4.709530 | 6.364751 | 11.135752 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 0 |
| 14719 | SC | 14.0 | 131704.247619 | 7818.752381 | 45.0 | 12.0 | 0.4 | 0.5 | 38 | 1.9 | 12 | 14 | 55 | 0.4 | 12 | 1.4 | 11.845985 | 3.209361 | 5.525453 | 6.924612 | 11.788314 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 0 |
| 14720 | SC | 15.0 | 220806.750000 | 15301.250000 | 45.0 | 12.0 | 0.4 | 0.5 | 38 | 1.9 | 12 | 14 | 55 | 0.4 | 12 | 1.4 | 12.372045 | 1.949359 | 5.525453 | 4.955827 | 12.305043 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 1 | 0 |
| 14721 | SC | 13.0 | 123514.663543 | -5445.663543 | 45.0 | 12.0 | 0.4 | 0.5 | 38 | 1.9 | 12 | 14 | 55 | 0.4 | 12 | 1.4 | 11.679024 | 2.073644 | 4.043051 | 6.924612 | 11.724115 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 0 |
| 14722 | SC | 11.0 | 68374.480370 | 7756.519630 | 45.0 | 12.0 | 0.4 | 0.5 | 38 | 1.9 | 12 | 14 | 55 | 0.4 | 12 | 1.4 | 11.240211 | 4.370355 | 2.397895 | 7.161622 | 11.132755 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 0 |
| 14723 | SC | 14.0 | 191284.996653 | 22448.003347 | 45.0 | 12.0 | 0.4 | 0.5 | 38 | 1.9 | 12 | 14 | 55 | 0.4 | 12 | 1.4 | 12.272483 | 2.073644 | 5.525453 | 4.744932 | 12.161520 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 1 | 0 |
| 14724 | SC | 12.0 | 82024.692725 | -11832.692725 | 45.0 | 12.0 | 0.4 | 0.5 | 38 | 1.9 | 12 | 14 | 55 | 0.4 | 12 | 1.4 | 11.158990 | 0.447214 | 4.709530 | 7.161622 | 11.314776 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 0 |
| 14725 | SC | 12.0 | 92736.988124 | -1481.988124 | 45.0 | 12.0 | 0.4 | 0.5 | 38 | 1.9 | 12 | 14 | 55 | 0.4 | 12 | 1.4 | 11.421413 | 1.843909 | 6.363028 | 7.161622 | 11.437523 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 0 |
| 14726 | SC | 12.0 | 82024.692725 | 2846.307275 | 45.0 | 12.0 | 0.4 | 0.5 | 38 | 1.9 | 12 | 14 | 55 | 0.4 | 12 | 1.4 | 11.348888 | 2.302173 | 4.709530 | 6.601230 | 11.314776 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 0 |
| 14727 | SC | 9.0 | 60393.678745 | 2526.321255 | 45.0 | 12.0 | 0.4 | 0.5 | 38 | 1.9 | 12 | 14 | 55 | 0.4 | 12 | 1.4 | 11.049619 | 4.381780 | 3.850148 | 5.438079 | 11.008640 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 0 |
| 14728 | SC | 12.0 | 92736.988124 | -4600.988124 | 45.0 | 12.0 | 0.4 | 0.5 | 38 | 1.9 | 12 | 14 | 55 | 0.4 | 12 | 1.4 | 11.386636 | 3.619392 | 6.363028 | 4.955827 | 11.437523 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 0 |
| 14729 | SC | 12.0 | 82230.512591 | -4837.512591 | 45.0 | 12.0 | 0.4 | 0.5 | 38 | 1.9 | 12 | 14 | 55 | 0.4 | 12 | 1.4 | 11.256652 | 2.408319 | 5.575949 | 6.716595 | 11.317282 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 0 |
| 14730 | SC | 15.0 | 145901.900524 | 5188.099476 | 45.0 | 12.0 | 0.4 | 0.5 | 38 | 1.9 | 12 | 14 | 55 | 0.4 | 12 | 1.4 | 11.925631 | 4.037326 | 5.056246 | 7.524561 | 11.890690 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 0 |
| 14731 | SC | 9.0 | 60393.678745 | -5538.678745 | 45.0 | 12.0 | 0.4 | 0.5 | 38 | 1.9 | 12 | 14 | 55 | 0.4 | 12 | 1.4 | 10.912449 | 0.632456 | 3.850148 | 6.364751 | 11.008640 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 0 |
| 14732 | SC | 11.0 | 70448.164306 | -223.164306 | 45.0 | 12.0 | 0.4 | 0.5 | 38 | 1.9 | 12 | 14 | 55 | 0.4 | 12 | 1.4 | 11.159460 | 2.588436 | 2.197225 | 6.603944 | 11.162632 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 0 |
| 14733 | SC | 12.0 | 86109.914894 | 2805.085106 | 45.0 | 12.0 | 0.4 | 0.5 | 38 | 1.9 | 12 | 14 | 55 | 0.4 | 12 | 1.4 | 11.395436 | 0.000000 | 3.828641 | 6.601230 | 11.363380 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 0 |
| 14734 | SC | 11.0 | 68579.699653 | -6093.699653 | 45.0 | 12.0 | 0.4 | 0.5 | 38 | 1.9 | 12 | 14 | 55 | 0.4 | 12 | 1.4 | 11.042698 | 0.447214 | 4.709530 | 6.716595 | 11.135752 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 1 | 0 | 0 | 1 |
| 14735 | SC | 14.0 | 191284.996653 | -8965.996653 | 45.0 | 12.0 | 0.4 | 0.5 | 38 | 1.9 | 12 | 14 | 55 | 0.4 | 12 | 1.4 | 12.113513 | 1.949359 | 5.525453 | 6.548219 | 12.161520 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 1 | 0 |
| 14736 | SC | 15.0 | 149705.694175 | 8994.305825 | 45.0 | 12.0 | 0.4 | 0.5 | 38 | 1.9 | 12 | 14 | 55 | 0.4 | 12 | 1.4 | 11.974771 | 4.571652 | 5.521461 | 7.237778 | 11.916427 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 0 |
| 14737 | SC | 13.0 | 106116.637459 | -3184.637459 | 45.0 | 12.0 | 0.4 | 0.5 | 38 | 1.9 | 12 | 14 | 55 | 0.4 | 12 | 1.4 | 11.541824 | 2.738613 | 6.363028 | 6.924612 | 11.572294 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 0 |
| 14738 | SC | 11.0 | 79100.263771 | 6315.736229 | 45.0 | 12.0 | 0.4 | 0.5 | 38 | 1.9 | 12 | 14 | 55 | 0.4 | 12 | 1.4 | 11.355289 | 2.323790 | 6.363028 | 6.716595 | 11.278471 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 0 |
| 14739 | SC | 9.0 | 63564.446718 | 12932.553282 | 45.0 | 12.0 | 0.4 | 0.5 | 38 | 1.9 | 12 | 14 | 55 | 0.4 | 12 | 1.4 | 11.245007 | 3.492850 | 6.363028 | 6.716595 | 11.059810 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 0 |
| 14740 | SC | 10.0 | 70840.400546 | 6970.599454 | 45.0 | 12.0 | 0.4 | 0.5 | 38 | 1.9 | 12 | 14 | 55 | 0.4 | 12 | 1.4 | 11.262038 | 2.408319 | 6.363028 | 7.161622 | 11.168185 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 0 |
| 14741 | SC | 14.0 | 123721.136031 | 16400.863969 | 45.0 | 12.0 | 0.4 | 0.5 | 38 | 1.9 | 12 | 14 | 55 | 0.4 | 12 | 1.4 | 11.850269 | 3.065942 | 5.521461 | 7.524561 | 11.725785 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 0 |
| 14742 | SC | 12.0 | 88181.600575 | 68.399425 | 45.0 | 12.0 | 0.4 | 0.5 | 38 | 1.9 | 12 | 14 | 55 | 0.4 | 12 | 1.4 | 11.387929 | 2.024846 | 1.386294 | 6.601230 | 11.387154 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 0 |
| 14743 | SC | 11.0 | 68579.699653 | 21549.300347 | 45.0 | 12.0 | 0.4 | 0.5 | 38 | 1.9 | 12 | 14 | 55 | 0.4 | 12 | 1.4 | 11.408997 | 2.738613 | 4.709530 | 7.524561 | 11.135752 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 0 |
| 14744 | SC | 11.0 | 79100.263771 | -6873.263771 | 45.0 | 12.0 | 0.4 | 0.5 | 38 | 1.9 | 12 | 14 | 55 | 0.4 | 12 | 1.4 | 11.187569 | 0.447214 | 6.363028 | 5.811141 | 11.278471 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 0 |
| 14745 | SC | 14.0 | 123231.023460 | -8751.023460 | 45.0 | 12.0 | 0.4 | 0.5 | 38 | 1.9 | 12 | 14 | 55 | 0.4 | 12 | 1.4 | 11.648155 | 2.880972 | 4.110874 | 7.237778 | 11.721816 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 0 |
| 14746 | SC | 10.0 | 70840.400546 | -5103.400546 | 45.0 | 12.0 | 0.4 | 0.5 | 38 | 1.9 | 12 | 14 | 55 | 0.4 | 12 | 1.4 | 11.093417 | 3.033150 | 6.363028 | 6.663133 | 11.168185 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 0 |
| 14747 | SC | 13.0 | 101243.170958 | -9429.170958 | 45.0 | 12.0 | 0.4 | 0.5 | 38 | 1.9 | 12 | 14 | 55 | 0.4 | 12 | 1.4 | 11.427520 | 1.732051 | 5.056246 | 5.572154 | 11.525281 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 0 |
| 14748 | SC | 12.0 | 82230.512591 | 15139.487409 | 45.0 | 12.0 | 0.4 | 0.5 | 38 | 1.9 | 12 | 14 | 55 | 0.4 | 12 | 1.4 | 11.486273 | 3.949684 | 5.575949 | 6.716595 | 11.317282 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 0 |
| 14749 | SC | 15.0 | 149705.694175 | 8994.305825 | 45.0 | 12.0 | 0.4 | 0.5 | 38 | 1.9 | 12 | 14 | 55 | 0.4 | 12 | 1.4 | 11.974771 | 2.509980 | 5.521461 | 7.237778 | 11.916427 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 0 |
| 14750 | SC | 12.0 | 86664.158633 | 4590.841367 | 45.0 | 12.0 | 0.4 | 0.5 | 38 | 1.9 | 12 | 14 | 55 | 0.4 | 12 | 1.4 | 11.421413 | 0.547723 | 3.367296 | 6.364751 | 11.369796 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 0 |
| 14751 | SC | 15.0 | 149705.694175 | 8994.305825 | 45.0 | 12.0 | 0.4 | 0.5 | 38 | 1.9 | 12 | 14 | 55 | 0.4 | 12 | 1.4 | 11.974771 | 2.529822 | 5.521461 | 7.237778 | 11.916427 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 0 |
| 14752 | SC | 14.0 | 132526.000000 | 7596.000000 | 45.0 | 12.0 | 0.4 | 0.5 | 38 | 1.9 | 12 | 14 | 55 | 0.4 | 12 | 1.4 | 11.850269 | 4.183300 | 0.693147 | 7.524561 | 11.794534 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 0 |
| 14753 | SC | 12.0 | 82024.692725 | 12952.307275 | 45.0 | 12.0 | 0.4 | 0.5 | 38 | 1.9 | 12 | 14 | 55 | 0.4 | 12 | 1.4 | 11.461390 | 2.810694 | 4.709530 | 6.593045 | 11.314776 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 0 |
| 14754 | SC | 11.0 | 79100.263771 | 8543.736229 | 45.0 | 12.0 | 0.4 | 0.5 | 38 | 1.9 | 12 | 14 | 55 | 0.4 | 12 | 1.4 | 11.381038 | 3.577709 | 6.363028 | 6.716595 | 11.278471 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 0 |
| 14755 | SC | 13.0 | 102899.534346 | 15169.465654 | 45.0 | 12.0 | 0.4 | 0.5 | 38 | 1.9 | 12 | 14 | 55 | 0.4 | 12 | 1.4 | 11.679024 | 2.408319 | 4.852030 | 6.924612 | 11.541508 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 0 |
| 14756 | SC | 13.0 | 104210.023553 | -1267.023553 | 45.0 | 12.0 | 0.4 | 0.5 | 38 | 1.9 | 12 | 14 | 55 | 0.4 | 12 | 1.4 | 11.541931 | 4.604346 | 2.197225 | 6.603944 | 11.554164 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 0 |
| 14757 | SC | 15.0 | 220806.750000 | -119176.750000 | 45.0 | 12.0 | 0.4 | 0.5 | 38 | 1.9 | 12 | 14 | 55 | 0.4 | 12 | 1.4 | 11.529094 | 0.447214 | 5.525453 | 6.322565 | 12.305043 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 1 | 0 |
| 14758 | SC | 11.0 | 70358.898088 | 8233.101912 | 45.0 | 12.0 | 0.4 | 0.5 | 38 | 1.9 | 12 | 14 | 55 | 0.4 | 12 | 1.4 | 11.272025 | 4.774935 | 4.605170 | 6.924612 | 11.161365 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 0 |
| 14759 | SC | 11.0 | 68579.699653 | -2266.699653 | 45.0 | 12.0 | 0.4 | 0.5 | 38 | 1.9 | 12 | 14 | 55 | 0.4 | 12 | 1.4 | 11.102141 | 0.836660 | 4.709530 | 6.144186 | 11.135752 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 0 |
| 14760 | SC | 11.0 | 79100.263771 | -14682.263771 | 45.0 | 12.0 | 0.4 | 0.5 | 38 | 1.9 | 12 | 14 | 55 | 0.4 | 12 | 1.4 | 11.073148 | 2.167948 | 6.363028 | 6.603944 | 11.278471 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 0 |
| 14761 | SC | 11.0 | 68579.699653 | 4321.300347 | 45.0 | 12.0 | 0.4 | 0.5 | 38 | 1.9 | 12 | 14 | 55 | 0.4 | 12 | 1.4 | 11.196858 | 2.774887 | 4.709530 | 6.716595 | 11.135752 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 0 |
| 14762 | SC | 13.0 | 106116.637459 | -19866.637459 | 45.0 | 12.0 | 0.4 | 0.5 | 38 | 1.9 | 12 | 14 | 55 | 0.4 | 12 | 1.4 | 11.365005 | 2.121320 | 6.363028 | 6.663133 | 11.572294 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 0 |
| 14763 | SC | 12.0 | 92736.988124 | 15935.011876 | 45.0 | 12.0 | 0.4 | 0.5 | 38 | 1.9 | 12 | 14 | 55 | 0.4 | 12 | 1.4 | 11.596089 | 3.847077 | 6.363028 | 7.524561 | 11.437523 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 0 |
| 14764 | SC | 11.0 | 68579.699653 | -4827.699653 | 45.0 | 12.0 | 0.4 | 0.5 | 38 | 1.9 | 12 | 14 | 55 | 0.4 | 12 | 1.4 | 11.062756 | 0.447214 | 4.709530 | 5.493061 | 11.135752 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 0 |
| 14765 | SC | 14.0 | 126211.943693 | 13311.056307 | 50.0 | 7.0 | 0.4 | 0.5 | 38 | 1.9 | 12 | 14 | 55 | 0.4 | 12 | 1.4 | 11.845985 | 2.549510 | 4.852030 | 6.924612 | 11.745718 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 0 |
| 14766 | SC | 13.0 | 106116.637459 | -11520.637459 | 50.0 | 7.0 | 0.4 | 0.5 | 38 | 1.9 | 12 | 14 | 55 | 0.4 | 12 | 1.4 | 11.457370 | 1.000000 | 6.363028 | 6.663133 | 11.572294 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 0 |
| 14767 | SC | 11.0 | 70358.898088 | 8233.101912 | 50.0 | 7.0 | 0.4 | 0.5 | 38 | 1.9 | 12 | 14 | 55 | 0.4 | 12 | 1.4 | 11.272025 | 3.834058 | 4.605170 | 6.924612 | 11.161365 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 0 |
| 14768 | SC | 14.0 | 123721.136031 | -14336.136031 | 50.0 | 7.0 | 0.4 | 0.5 | 38 | 1.9 | 12 | 14 | 55 | 0.4 | 12 | 1.4 | 11.602629 | 1.140175 | 5.521461 | 6.663133 | 11.725785 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 0 |
| 14769 | SC | 14.0 | 191284.996653 | -17714.996653 | 50.0 | 7.0 | 0.4 | 0.5 | 38 | 1.9 | 12 | 14 | 55 | 0.4 | 12 | 1.4 | 12.064336 | 2.489980 | 5.525453 | 6.924612 | 12.161520 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 1 |
| 14770 | SC | 11.0 | 79100.263771 | -14682.263771 | 50.0 | 7.0 | 0.4 | 0.5 | 38 | 1.9 | 12 | 14 | 55 | 0.4 | 12 | 1.4 | 11.073148 | 2.449490 | 6.363028 | 5.313206 | 11.278471 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 0 |
| 14771 | SC | 11.0 | 68579.699653 | 3647.300347 | 50.0 | 7.0 | 0.4 | 0.5 | 38 | 1.9 | 12 | 14 | 55 | 0.4 | 12 | 1.4 | 11.187569 | 3.255764 | 4.709530 | 6.663133 | 11.135752 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 0 |
| 14772 | SC | 9.0 | 56010.448864 | -4381.448864 | 50.0 | 7.0 | 0.4 | 0.5 | 38 | 1.9 | 12 | 14 | 55 | 0.4 | 12 | 1.4 | 10.851839 | 2.213594 | 2.302585 | 7.172425 | 10.933294 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 0 |
| 14773 | SC | 11.0 | 68766.790598 | -4348.790598 | 50.0 | 7.0 | 0.4 | 0.5 | 38 | 1.9 | 12 | 14 | 55 | 0.4 | 12 | 1.4 | 11.073148 | 3.098387 | 2.197225 | 6.603944 | 11.138476 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 0 |
| 14774 | SC | 7.0 | 68483.884615 | 453.115385 | 50.0 | 7.0 | 0.4 | 0.5 | 38 | 1.9 | 12 | 14 | 55 | 0.4 | 12 | 1.4 | 11.140948 | 0.547723 | 3.713572 | 7.172425 | 11.134354 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 0 |
| 14775 | SC | 8.0 | 57091.339212 | -3040.339212 | 50.0 | 7.0 | 0.4 | 0.5 | 38 | 1.9 | 12 | 14 | 55 | 0.4 | 12 | 1.4 | 10.897683 | 2.792848 | 4.852030 | 6.548219 | 10.952408 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 0 |
| 14776 | SC | 13.0 | 103634.830709 | -17384.830709 | 50.0 | 7.0 | 0.4 | 0.5 | 38 | 1.9 | 12 | 14 | 55 | 0.4 | 12 | 1.4 | 11.365005 | 1.000000 | 2.079442 | 6.663133 | 11.548629 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 0 |
| 14777 | SC | 13.0 | 106116.637459 | -22648.637459 | 50.0 | 7.0 | 0.4 | 0.5 | 38 | 1.9 | 12 | 14 | 55 | 0.4 | 12 | 1.4 | 11.332219 | 1.788854 | 6.363028 | 5.811141 | 11.572294 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 0 |
| 14778 | SC | 9.0 | 60393.678745 | 3672.321255 | 50.0 | 7.0 | 0.4 | 0.5 | 38 | 1.9 | 12 | 14 | 55 | 0.4 | 12 | 1.4 | 11.067669 | 0.547723 | 3.850148 | 6.663133 | 11.008640 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 0 |
| 14779 | SC | 10.0 | 70840.400546 | -6880.400546 | 50.0 | 7.0 | 0.4 | 0.5 | 38 | 1.9 | 12 | 14 | 55 | 0.4 | 12 | 1.4 | 11.066013 | 1.000000 | 6.363028 | 5.811141 | 11.168185 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 0 |
| 14780 | SC | 9.0 | 55536.787879 | -2669.787879 | 50.0 | 7.0 | 0.4 | 0.5 | 38 | 1.9 | 12 | 14 | 55 | 0.4 | 12 | 1.4 | 10.875535 | 0.000000 | 1.098612 | 6.285998 | 10.924801 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 1 |
| 14781 | SC | 15.0 | 149705.694175 | 8994.305825 | 50.0 | 7.0 | 0.4 | 0.5 | 38 | 1.9 | 12 | 14 | 55 | 0.4 | 12 | 1.4 | 11.974771 | 3.860052 | 5.521461 | 7.237778 | 11.916427 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 0 |
| 14782 | SC | 11.0 | 79100.263771 | -11520.263771 | 50.0 | 7.0 | 0.4 | 0.5 | 38 | 1.9 | 12 | 14 | 55 | 0.4 | 12 | 1.4 | 11.121067 | 2.073644 | 6.363028 | 6.255750 | 11.278471 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 0 |
| 14783 | SC | 11.0 | 79100.263771 | -442.263771 | 50.0 | 7.0 | 0.4 | 0.5 | 38 | 1.9 | 12 | 14 | 55 | 0.4 | 12 | 1.4 | 11.272865 | 2.701851 | 6.363028 | 6.548219 | 11.278471 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 0 |
| 14784 | SC | 11.0 | 70358.898088 | 5772.101912 | 50.0 | 7.0 | 0.4 | 0.5 | 38 | 1.9 | 12 | 14 | 55 | 0.4 | 12 | 1.4 | 11.240211 | 2.509980 | 4.605170 | 6.354370 | 11.161365 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 0 |
| 14785 | SC | 12.0 | 82024.692725 | 5636.307275 | 50.0 | 7.0 | 0.4 | 0.5 | 38 | 1.9 | 12 | 14 | 55 | 0.4 | 12 | 1.4 | 11.381232 | 2.073644 | 4.709530 | 7.524561 | 11.314776 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 0 |
| 14786 | SC | 12.0 | 82024.692725 | 12178.307275 | 50.0 | 7.0 | 0.4 | 0.5 | 38 | 1.9 | 12 | 14 | 55 | 0.4 | 12 | 1.4 | 11.453207 | 5.079370 | 4.709530 | 7.172425 | 11.314776 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 0 |
| 14787 | SC | 13.0 | 106116.637459 | -8284.637459 | 50.0 | 7.0 | 0.4 | 0.5 | 38 | 1.9 | 12 | 14 | 55 | 0.4 | 12 | 1.4 | 11.491007 | 1.140175 | 6.363028 | 7.161622 | 11.572294 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 0 |
| 14788 | SC | 12.0 | 92736.988124 | -3935.988124 | 50.0 | 7.0 | 0.4 | 0.5 | 38 | 1.9 | 12 | 14 | 55 | 0.4 | 12 | 1.4 | 11.394153 | 3.847077 | 6.363028 | 6.354370 | 11.437523 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 0 |
| 14789 | SC | 11.0 | 79100.263771 | -10702.263771 | 50.0 | 7.0 | 0.4 | 0.5 | 38 | 1.9 | 12 | 14 | 55 | 0.4 | 12 | 1.4 | 11.133099 | 1.048809 | 6.363028 | 6.322565 | 11.278471 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 0 |
| 14790 | SC | 12.0 | 144554.953704 | -9554.953704 | 50.0 | 7.0 | 0.4 | 0.5 | 38 | 1.9 | 12 | 14 | 55 | 0.4 | 12 | 1.4 | 11.813030 | 0.707107 | 2.772589 | 6.548219 | 11.881415 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 1 | 0 |
| 14791 | SC | 15.0 | 149705.694175 | 15169.305825 | 50.0 | 7.0 | 0.4 | 0.5 | 38 | 1.9 | 12 | 14 | 55 | 0.4 | 12 | 1.4 | 12.012943 | 2.932576 | 5.521461 | 7.172425 | 11.916427 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 0 |
| 14792 | SC | 11.0 | 79100.263771 | -8825.263771 | 50.0 | 7.0 | 0.4 | 0.5 | 38 | 1.9 | 12 | 14 | 55 | 0.4 | 12 | 1.4 | 11.160171 | 0.632456 | 6.363028 | 5.572154 | 11.278471 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 0 |
| 14793 | SC | 13.0 | 98098.882963 | -3502.882963 | 50.0 | 7.0 | 0.4 | 0.5 | 38 | 1.9 | 12 | 14 | 55 | 0.4 | 12 | 1.4 | 11.457370 | 3.114482 | 4.709530 | 6.013715 | 11.493731 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 0 |
| 14794 | SC | 9.0 | 52638.589928 | 12302.410072 | 50.0 | 7.0 | 0.4 | 0.5 | 38 | 1.9 | 12 | 14 | 55 | 0.4 | 12 | 1.4 | 11.081234 | 2.509980 | 3.218876 | 7.524561 | 10.871205 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 1 | 0 | 0 | 1 |
| 14795 | SC | 14.0 | 127403.579634 | 4964.420366 | 50.0 | 7.0 | 0.4 | 0.5 | 38 | 1.9 | 12 | 14 | 55 | 0.4 | 12 | 1.4 | 11.793341 | 5.176872 | 4.077537 | 6.924612 | 11.755115 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 0 |
| 14796 | SC | 11.0 | 70358.898088 | -83.898088 | 50.0 | 7.0 | 0.4 | 0.5 | 38 | 1.9 | 12 | 14 | 55 | 0.4 | 12 | 1.4 | 11.160171 | 5.412947 | 4.605170 | 7.172425 | 11.161365 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 0 |
| 14797 | SC | 13.0 | 109912.503580 | 5666.496420 | 50.0 | 7.0 | 0.4 | 0.5 | 38 | 1.9 | 12 | 14 | 55 | 0.4 | 12 | 1.4 | 11.657710 | 5.329165 | 2.890372 | 6.001415 | 11.607440 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 0 |
| 14798 | SC | 13.0 | 103267.301887 | -11453.301887 | 50.0 | 7.0 | 0.4 | 0.5 | 38 | 1.9 | 12 | 14 | 55 | 0.4 | 12 | 1.4 | 11.427520 | 1.816590 | 1.386294 | 5.438079 | 11.545076 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 0 |
| 14799 | SC | 12.0 | 86959.975696 | 4811.024304 | 50.0 | 7.0 | 0.4 | 0.5 | 38 | 1.9 | 12 | 14 | 55 | 0.4 | 12 | 1.4 | 11.427052 | 5.504544 | 4.127134 | 6.011267 | 11.373203 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 0 |
| 14800 | SC | 11.0 | 79100.263771 | -1222.263771 | 50.0 | 7.0 | 0.4 | 0.5 | 38 | 1.9 | 12 | 14 | 55 | 0.4 | 12 | 1.4 | 11.262899 | 2.000000 | 6.363028 | 7.161622 | 11.278471 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 0 |
| 14801 | SC | 14.0 | 120594.342736 | -2234.342736 | 50.0 | 7.0 | 0.4 | 0.5 | 38 | 1.9 | 12 | 14 | 55 | 0.4 | 12 | 1.4 | 11.681486 | 4.277850 | 5.056246 | 6.603944 | 11.700188 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 0 |
| 14802 | SC | 13.0 | 102899.534346 | 5567.465654 | 50.0 | 7.0 | 0.4 | 0.5 | 38 | 1.9 | 12 | 14 | 55 | 0.4 | 12 | 1.4 | 11.594201 | 2.880972 | 4.852030 | 7.524561 | 11.541508 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 1 | 0 | 0 | 1 |
| 14803 | SC | 12.0 | 82230.512591 | 9179.487409 | 50.0 | 7.0 | 0.4 | 0.5 | 38 | 1.9 | 12 | 14 | 55 | 0.4 | 12 | 1.4 | 11.423110 | 2.236068 | 5.575949 | 7.524561 | 11.317282 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 0 |
| 14804 | SC | 13.0 | 105047.296509 | -13233.296509 | 50.0 | 7.0 | 0.4 | 0.5 | 38 | 1.9 | 12 | 14 | 55 | 0.4 | 12 | 1.4 | 11.427520 | 3.316625 | 4.804021 | 6.603944 | 11.562166 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 0 |
| 14805 | SC | 15.0 | 155755.535242 | -4925.535242 | 50.0 | 7.0 | 0.4 | 0.5 | 38 | 1.9 | 12 | 14 | 55 | 0.4 | 12 | 1.4 | 11.923909 | 0.316228 | 5.525453 | 6.322565 | 11.956043 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 1 |
| 14806 | SC | 12.0 | 87049.140351 | -9836.140351 | 50.0 | 7.0 | 0.4 | 0.5 | 38 | 1.9 | 12 | 14 | 55 | 0.4 | 12 | 1.4 | 11.254323 | 1.000000 | 2.995732 | 6.364751 | 11.374228 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 0 |
| 14807 | SC | 14.0 | 191284.996653 | -22262.996653 | 50.0 | 7.0 | 0.4 | 0.5 | 38 | 1.9 | 12 | 14 | 55 | 0.4 | 12 | 1.4 | 12.037784 | 2.302173 | 5.525453 | 5.572154 | 12.161520 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 1 | 0 |
| 14808 | SC | 12.0 | 85614.272727 | 11501.727273 | 50.0 | 7.0 | 0.4 | 0.5 | 38 | 1.9 | 12 | 14 | 55 | 0.4 | 12 | 1.4 | 11.483661 | 4.219005 | 3.496508 | 6.285998 | 11.357607 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 0 |
| 14809 | SC | 12.0 | 82024.692725 | 2209.307275 | 50.0 | 7.0 | 0.4 | 0.5 | 38 | 1.9 | 12 | 14 | 55 | 0.4 | 12 | 1.4 | 11.341354 | 4.098780 | 4.709530 | 6.548219 | 11.314776 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 0 |
| 14810 | SC | 10.0 | 67711.394247 | 4313.605753 | 50.0 | 7.0 | 0.4 | 0.5 | 38 | 1.9 | 12 | 14 | 55 | 0.4 | 12 | 1.4 | 11.184769 | 3.162278 | 3.850148 | 6.601230 | 11.123010 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 0 |
| 14811 | SC | 11.0 | 79883.944848 | 928.055152 | 50.0 | 7.0 | 0.4 | 0.5 | 38 | 1.9 | 12 | 14 | 55 | 0.4 | 12 | 1.4 | 11.299881 | 1.732051 | 3.713572 | 7.172425 | 11.288330 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 0 |
| 14812 | SC | 15.0 | 150765.631396 | 7934.368604 | 50.0 | 7.0 | 0.4 | 0.5 | 38 | 1.9 | 12 | 14 | 55 | 0.4 | 12 | 1.4 | 11.974771 | 3.521363 | 4.852030 | 6.924612 | 11.923482 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 0 |
| 14813 | SC | 9.0 | 63564.446718 | 4190.553282 | 50.0 | 7.0 | 0.4 | 0.5 | 38 | 1.9 | 12 | 14 | 55 | 0.4 | 12 | 1.4 | 11.123654 | 2.236068 | 6.363028 | 5.493061 | 11.059810 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 0 |
| 14814 | SC | 12.0 | 92736.988124 | -17863.988124 | 50.0 | 7.0 | 0.4 | 0.5 | 38 | 1.9 | 12 | 14 | 55 | 0.4 | 12 | 1.4 | 11.223549 | 1.449138 | 6.363028 | 6.013715 | 11.437523 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 0 |
| 14815 | SC | 13.0 | 99172.922519 | 18896.077481 | 50.0 | 7.0 | 0.4 | 0.5 | 38 | 1.9 | 12 | 14 | 55 | 0.4 | 12 | 1.4 | 11.679024 | 2.213594 | 5.575949 | 7.237778 | 11.504620 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 0 |
| 14816 | SC | 11.0 | 77439.725728 | -1428.725728 | 50.0 | 7.0 | 0.4 | 0.5 | 38 | 1.9 | 12 | 14 | 55 | 0.4 | 12 | 1.4 | 11.238633 | 2.664583 | 3.555348 | 6.593045 | 11.257255 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 0 |
| 14817 | SC | 9.0 | 55386.370000 | -4359.370000 | 50.0 | 7.0 | 0.4 | 0.5 | 38 | 1.9 | 12 | 14 | 55 | 0.4 | 12 | 1.4 | 10.840110 | 3.591657 | 3.988984 | 6.354370 | 10.922089 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 0 |
| 14818 | SC | 12.0 | 83907.539514 | 508.460486 | 50.0 | 7.0 | 0.4 | 0.5 | 38 | 1.9 | 12 | 14 | 55 | 0.4 | 12 | 1.4 | 11.343512 | 2.121320 | 4.682131 | 7.161622 | 11.337471 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 0 |
| 14819 | SC | 8.0 | 57091.339212 | -6085.339212 | 50.0 | 7.0 | 0.4 | 0.5 | 38 | 1.9 | 12 | 14 | 55 | 0.4 | 12 | 1.4 | 10.839699 | 1.303840 | 4.852030 | 6.603944 | 10.952408 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 0 |
| 14820 | SC | 12.0 | 92736.988124 | 4267.011876 | 50.0 | 7.0 | 0.4 | 0.5 | 38 | 1.9 | 12 | 14 | 55 | 0.4 | 12 | 1.4 | 11.482507 | 2.626785 | 6.363028 | 6.255750 | 11.437523 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 0 |
| 14821 | SC | 12.0 | 82024.692725 | -130.692725 | 50.0 | 7.0 | 0.4 | 0.5 | 38 | 1.9 | 12 | 14 | 55 | 0.4 | 12 | 1.4 | 11.313181 | 3.507136 | 4.709530 | 6.274762 | 11.314776 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 0 |
| 14822 | SC | 9.0 | 53549.699170 | -936.699170 | 50.0 | 7.0 | 0.4 | 0.5 | 38 | 1.9 | 12 | 14 | 55 | 0.4 | 12 | 1.4 | 10.870719 | 0.948683 | 4.682131 | 5.891644 | 10.888365 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 0 |
| 14823 | SC | 12.0 | 83793.716763 | 2212.283237 | 50.0 | 7.0 | 0.4 | 0.5 | 38 | 1.9 | 12 | 14 | 55 | 0.4 | 12 | 1.4 | 11.362172 | 2.607681 | 2.995732 | 7.524561 | 11.336113 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 0 |
| 14824 | SC | 15.0 | 151253.086498 | -3965.086498 | 50.0 | 7.0 | 0.4 | 0.5 | 38 | 1.9 | 12 | 14 | 55 | 0.4 | 12 | 1.4 | 11.900145 | 2.190890 | 4.804021 | 7.172425 | 11.926710 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 1 | 0 |
| 14825 | SC | 11.0 | 68579.699653 | 7551.300347 | 50.0 | 7.0 | 0.4 | 0.5 | 38 | 1.9 | 12 | 14 | 55 | 0.4 | 12 | 1.4 | 11.240211 | 2.236068 | 4.709530 | 4.709530 | 11.135752 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 0 |
| 14826 | SC | 9.0 | 75003.755877 | 1339.244123 | 50.0 | 7.0 | 0.4 | 0.5 | 38 | 1.9 | 12 | 14 | 55 | 0.4 | 12 | 1.4 | 11.242992 | 0.707107 | 3.713572 | 6.663133 | 11.225293 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 0 |
| 14827 | SC | 11.0 | 69488.327856 | -1166.327856 | 50.0 | 7.0 | 0.4 | 0.5 | 38 | 1.9 | 12 | 14 | 55 | 0.4 | 12 | 1.4 | 11.131987 | 3.065942 | 4.304065 | 7.161622 | 11.148914 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 0 |
| 14828 | SC | 11.0 | 70814.195513 | 12612.804487 | 50.0 | 7.0 | 0.4 | 0.5 | 38 | 1.9 | 12 | 14 | 55 | 0.4 | 12 | 1.4 | 11.331727 | 3.346640 | 2.302585 | 6.144186 | 11.167815 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 1 |
| 14829 | SC | 9.0 | 55821.734185 | -3153.734185 | 50.0 | 7.0 | 0.4 | 0.5 | 38 | 1.9 | 12 | 14 | 55 | 0.4 | 12 | 1.4 | 10.871763 | 2.408319 | 4.605170 | 6.924612 | 10.929919 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 0 |
| 14830 | SC | 12.0 | 85106.739617 | -10233.739617 | 50.0 | 7.0 | 0.4 | 0.5 | 38 | 1.9 | 12 | 14 | 55 | 0.4 | 12 | 1.4 | 11.223549 | 2.701851 | 4.605170 | 7.172425 | 11.351662 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 0 |
| 14831 | SC | 13.0 | 101243.170958 | 1699.829042 | 55.0 | 2.0 | 0.4 | 0.5 | 38 | 1.9 | 12 | 14 | 55 | 0.4 | 12 | 1.4 | 11.541931 | 0.948683 | 5.056246 | 6.593045 | 11.525281 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 0 |
| 14832 | SC | 11.0 | 63592.893443 | -5030.893443 | 55.0 | 2.0 | 0.4 | 0.5 | 38 | 1.9 | 12 | 14 | 55 | 0.4 | 12 | 1.4 | 10.977841 | 0.000000 | 2.639057 | 6.011267 | 11.060257 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 1 |
| 14833 | SC | 12.0 | 92736.988124 | -10260.988124 | 55.0 | 2.0 | 0.4 | 0.5 | 38 | 1.9 | 12 | 14 | 55 | 0.4 | 12 | 1.4 | 11.320263 | 0.894427 | 6.363028 | 7.172425 | 11.437523 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 0 |
| 14834 | SC | 14.0 | 124044.275492 | -13142.275492 | 55.0 | 2.0 | 0.4 | 0.5 | 38 | 1.9 | 12 | 14 | 55 | 0.4 | 12 | 1.4 | 11.616402 | 1.224745 | 4.304065 | 7.237778 | 11.728394 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 1 |
| 14835 | SC | 11.0 | 68579.699653 | -8065.699653 | 55.0 | 2.0 | 0.4 | 0.5 | 38 | 1.9 | 12 | 14 | 55 | 0.4 | 12 | 1.4 | 11.010630 | 1.341641 | 4.709530 | 5.811141 | 11.135752 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 0 |
| 14836 | SC | 15.0 | 220806.750000 | -33624.750000 | 55.0 | 2.0 | 0.4 | 0.5 | 38 | 1.9 | 12 | 14 | 55 | 0.4 | 12 | 1.4 | 12.139837 | 2.258318 | 5.525453 | 6.001415 | 12.305043 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 1 | 0 |
| 14837 | SC | 9.0 | 56537.896671 | -6521.896671 | 55.0 | 2.0 | 0.4 | 0.5 | 38 | 1.9 | 12 | 14 | 55 | 0.4 | 12 | 1.4 | 10.820098 | 2.236068 | 4.304065 | 7.172425 | 10.942666 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 0 |
| 14838 | SC | 9.0 | 75003.755877 | 1339.244123 | 55.0 | 2.0 | 0.4 | 0.5 | 38 | 1.9 | 12 | 14 | 55 | 0.4 | 12 | 1.4 | 11.242992 | 1.341641 | 3.713572 | 7.172425 | 11.225293 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 0 |
| 14839 | SC | 15.0 | 149938.117647 | 8761.882353 | 55.0 | 2.0 | 0.4 | 0.5 | 38 | 1.9 | 12 | 14 | 55 | 0.4 | 12 | 1.4 | 11.974771 | 2.469818 | 0.693147 | 7.237778 | 11.917978 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 0 |
| 14840 | SC | 14.0 | 191284.996653 | 6754.003347 | 55.0 | 2.0 | 0.4 | 0.5 | 38 | 1.9 | 12 | 14 | 55 | 0.4 | 12 | 1.4 | 12.196219 | 1.816590 | 5.525453 | 6.924612 | 12.161520 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 1 |
| 14841 | SC | 10.0 | 70840.400546 | -2365.400546 | 55.0 | 2.0 | 0.4 | 0.5 | 38 | 1.9 | 12 | 14 | 55 | 0.4 | 12 | 1.4 | 11.134224 | 3.301515 | 6.363028 | 6.548219 | 11.168185 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 0 |
| 14842 | SC | 12.0 | 92736.988124 | -22544.988124 | 55.0 | 2.0 | 0.4 | 0.5 | 38 | 1.9 | 12 | 14 | 55 | 0.4 | 12 | 1.4 | 11.158990 | 1.581139 | 6.363028 | 5.572154 | 11.437523 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 0 |
| 14843 | SC | 12.0 | 85699.737850 | -1465.737850 | 55.0 | 2.0 | 0.4 | 0.5 | 38 | 1.9 | 12 | 14 | 55 | 0.4 | 12 | 1.4 | 11.341354 | 2.966479 | 3.850148 | 7.161622 | 11.358605 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 0 |
| 14844 | SC | 9.0 | 60393.678745 | -8103.678745 | 55.0 | 2.0 | 0.4 | 0.5 | 38 | 1.9 | 12 | 14 | 55 | 0.4 | 12 | 1.4 | 10.864560 | 3.361547 | 3.850148 | 6.144186 | 11.008640 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 0 |
| 14845 | SC | 13.0 | 106942.574648 | 11126.425352 | 55.0 | 2.0 | 0.4 | 0.5 | 38 | 1.9 | 12 | 14 | 55 | 0.4 | 12 | 1.4 | 11.679024 | 2.645751 | 4.804021 | 6.924612 | 11.580047 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 1 | 0 |
| 14846 | SC | 12.0 | 80439.445814 | 243.554186 | 55.0 | 2.0 | 0.4 | 0.5 | 38 | 1.9 | 12 | 14 | 55 | 0.4 | 12 | 1.4 | 11.298283 | 3.193744 | 5.521461 | 6.364751 | 11.295260 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 0 |
| 14847 | SC | 13.0 | 99740.024631 | 420.975369 | 55.0 | 2.0 | 0.4 | 0.5 | 38 | 1.9 | 12 | 14 | 55 | 0.4 | 12 | 1.4 | 11.514534 | 3.271085 | 2.397895 | 5.313206 | 11.510322 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 0 |
| 14848 | SC | 11.0 | 79100.263771 | 4259.736229 | 55.0 | 2.0 | 0.4 | 0.5 | 38 | 1.9 | 12 | 14 | 55 | 0.4 | 12 | 1.4 | 11.330924 | 1.000000 | 6.363028 | 6.548219 | 11.278471 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 0 |
| 14849 | SC | 12.0 | 83930.539259 | -11397.539259 | 55.0 | 2.0 | 0.4 | 0.5 | 38 | 1.9 | 12 | 14 | 55 | 0.4 | 12 | 1.4 | 11.191797 | 1.378405 | 4.304065 | 6.663133 | 11.337745 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 0 |
| 14850 | SC | 11.0 | 65335.534091 | -6773.534091 | 55.0 | 2.0 | 0.4 | 0.5 | 38 | 1.9 | 12 | 14 | 55 | 0.4 | 12 | 1.4 | 10.977841 | 1.048809 | 3.891820 | 5.438079 | 11.087291 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 0 |
| 14851 | SC | 12.0 | 82024.692725 | -7151.692725 | 55.0 | 2.0 | 0.4 | 0.5 | 38 | 1.9 | 12 | 14 | 55 | 0.4 | 12 | 1.4 | 11.223549 | 1.140175 | 4.709530 | 6.603944 | 11.314776 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 0 |
| 14852 | SC | 12.0 | 83145.080000 | -5932.080000 | 55.0 | 2.0 | 0.4 | 0.5 | 38 | 1.9 | 12 | 14 | 55 | 0.4 | 12 | 1.4 | 11.254323 | 3.987480 | 1.791759 | 6.013715 | 11.328342 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 0 |
| 14853 | SC | 14.0 | 132526.000000 | 7596.000000 | 55.0 | 2.0 | 0.4 | 0.5 | 38 | 1.9 | 12 | 14 | 55 | 0.4 | 12 | 1.4 | 11.850269 | 3.146427 | 0.693147 | 7.524561 | 11.794534 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 0 |
| 14854 | SC | 12.0 | 117199.743701 | -12663.743701 | 55.0 | 2.0 | 0.4 | 0.5 | 38 | 1.9 | 12 | 14 | 55 | 0.4 | 12 | 1.4 | 11.557287 | 0.547723 | 4.043051 | 7.172425 | 11.671635 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 0 |
| 14855 | SC | 11.0 | 79883.944848 | 928.055152 | 55.0 | 2.0 | 0.4 | 0.5 | 38 | 1.9 | 12 | 14 | 55 | 0.4 | 12 | 1.4 | 11.299881 | 1.095445 | 3.713572 | 7.172425 | 11.288330 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 0 |
| 14856 | SC | 10.0 | 70840.400546 | -12209.400546 | 55.0 | 2.0 | 0.4 | 0.5 | 38 | 1.9 | 12 | 14 | 55 | 0.4 | 12 | 1.4 | 10.979019 | 2.323790 | 6.363028 | 5.697093 | 11.168185 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 0 |
| 14857 | SC | 13.0 | 100997.389081 | -10514.389081 | 55.0 | 2.0 | 0.4 | 0.5 | 38 | 1.9 | 12 | 14 | 55 | 0.4 | 12 | 1.4 | 11.412917 | 2.792848 | 3.555348 | 5.846439 | 11.522850 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 0 |
| 14858 | SC | 9.0 | 63564.446718 | -9861.446718 | 55.0 | 2.0 | 0.4 | 0.5 | 38 | 1.9 | 12 | 14 | 55 | 0.4 | 12 | 1.4 | 10.891224 | 1.048809 | 6.363028 | 7.161622 | 11.059810 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 0 |
| 14859 | SC | 14.0 | 130040.356529 | 15513.643471 | 55.0 | 2.0 | 0.4 | 0.5 | 38 | 1.9 | 12 | 14 | 55 | 0.4 | 12 | 1.4 | 11.888302 | 5.157519 | 3.713572 | 7.161622 | 11.775600 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 0 |
| 14860 | SC | 11.0 | 73378.557153 | 5213.442847 | 55.0 | 2.0 | 0.4 | 0.5 | 38 | 1.9 | 12 | 14 | 55 | 0.4 | 12 | 1.4 | 11.272025 | 4.888763 | 3.850148 | 6.924612 | 11.203387 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 0 |
| 14861 | SC | 13.0 | 105724.708136 | -5998.708136 | 55.0 | 2.0 | 0.4 | 0.5 | 38 | 1.9 | 12 | 14 | 55 | 0.4 | 12 | 1.4 | 11.510182 | 2.323790 | 3.828641 | 6.354370 | 11.568594 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 0 |
| 14862 | SC | 10.0 | 70840.400546 | 5159.599454 | 55.0 | 2.0 | 0.4 | 0.5 | 38 | 1.9 | 12 | 14 | 55 | 0.4 | 12 | 1.4 | 11.238489 | 2.569047 | 6.363028 | 7.161622 | 11.168185 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 0 |
| 14863 | SC | 11.0 | 63592.893443 | -5030.893443 | 55.0 | 2.0 | 0.4 | 0.5 | 38 | 1.9 | 12 | 14 | 55 | 0.4 | 12 | 1.4 | 10.977841 | 0.000000 | 2.639057 | 6.354370 | 11.060257 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 1 |
| 14864 | SC | 11.0 | 64211.566372 | 206.433628 | 55.0 | 2.0 | 0.4 | 0.5 | 38 | 1.9 | 12 | 14 | 55 | 0.4 | 12 | 1.4 | 11.073148 | 0.836660 | 3.555348 | 5.313206 | 11.069939 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 0 |
| 14865 | SC | 12.0 | 82024.692725 | -11832.692725 | 55.0 | 2.0 | 0.4 | 0.5 | 38 | 1.9 | 12 | 14 | 55 | 0.4 | 12 | 1.4 | 11.158990 | 0.547723 | 4.709530 | 5.811141 | 11.314776 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 0 |
| 14866 | SC | 13.0 | 104862.598015 | -958.598015 | 55.0 | 2.0 | 0.4 | 0.5 | 38 | 1.9 | 12 | 14 | 55 | 0.4 | 12 | 1.4 | 11.551223 | 2.509980 | 2.639057 | 6.716595 | 11.560406 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 0 |
| 14867 | SC | 13.0 | 101243.170958 | 7263.829042 | 55.0 | 2.0 | 0.4 | 0.5 | 38 | 1.9 | 12 | 14 | 55 | 0.4 | 12 | 1.4 | 11.594570 | 2.792848 | 5.056246 | 6.255750 | 11.525281 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 0 |
| 14868 | SC | 13.0 | 103228.144628 | 7247.855372 | 55.0 | 2.0 | 0.4 | 0.5 | 38 | 1.9 | 12 | 14 | 55 | 0.4 | 12 | 1.4 | 11.612554 | 5.069517 | 2.484907 | 6.364751 | 11.544697 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 0 |
| 14869 | SC | 9.0 | 63564.446718 | 8485.553282 | 55.0 | 2.0 | 0.4 | 0.5 | 38 | 1.9 | 12 | 14 | 55 | 0.4 | 12 | 1.4 | 11.185116 | 4.159327 | 6.363028 | 6.548219 | 11.059810 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 0 |
| 14870 | SC | 13.0 | 101243.170958 | 7263.829042 | 55.0 | 2.0 | 0.4 | 0.5 | 38 | 1.9 | 12 | 14 | 55 | 0.4 | 12 | 1.4 | 11.594570 | 2.190890 | 5.056246 | 5.252273 | 11.525281 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 0 |
| 14871 | SC | 11.0 | 79100.263771 | -18586.263771 | 55.0 | 2.0 | 0.4 | 0.5 | 38 | 1.9 | 12 | 14 | 55 | 0.4 | 12 | 1.4 | 11.010630 | 1.000000 | 6.363028 | 6.603944 | 11.278471 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 0 |
| 14872 | SC | 13.0 | 106229.657976 | -14762.657976 | 55.0 | 2.0 | 0.4 | 0.5 | 38 | 1.9 | 12 | 14 | 55 | 0.4 | 12 | 1.4 | 11.423734 | 2.073644 | 3.496508 | 6.144186 | 11.573359 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 0 |
| 14873 | SC | 14.0 | 126574.859673 | 5793.140327 | 55.0 | 2.0 | 0.4 | 0.5 | 38 | 1.9 | 12 | 14 | 55 | 0.4 | 12 | 1.4 | 11.793341 | 3.193744 | 3.135494 | 6.924612 | 11.748589 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 0 |
| 14874 | SC | 15.0 | 151062.124675 | -7983.124675 | 55.0 | 2.0 | 0.4 | 0.5 | 38 | 1.9 | 12 | 14 | 55 | 0.4 | 12 | 1.4 | 11.871152 | 1.000000 | 3.555348 | 7.172425 | 11.925446 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 0 |
| 14875 | SC | 11.0 | 70501.568442 | 1252.431558 | 55.0 | 2.0 | 0.4 | 0.5 | 38 | 1.9 | 12 | 14 | 55 | 0.4 | 12 | 1.4 | 11.180999 | 1.949359 | 4.852030 | 7.524561 | 11.163390 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 0 |
| 14876 | SC | 11.0 | 70814.195513 | -8348.195513 | 60.0 | -3.0 | 0.4 | 0.5 | 38 | 1.9 | 12 | 14 | 55 | 0.4 | 12 | 1.4 | 11.042378 | 2.701851 | 2.302585 | 6.322565 | 11.167815 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 0 |
| 14877 | SC | 11.0 | 69488.327856 | 916.672144 | 60.0 | -3.0 | 0.4 | 0.5 | 38 | 1.9 | 12 | 14 | 55 | 0.4 | 12 | 1.4 | 11.162020 | 1.449138 | 4.304065 | 6.274762 | 11.148914 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 0 |
| 14878 | SC | 13.0 | 101243.170958 | 6325.829042 | 60.0 | -3.0 | 0.4 | 0.5 | 38 | 1.9 | 12 | 14 | 55 | 0.4 | 12 | 1.4 | 11.585888 | 1.000000 | 5.056246 | 6.364751 | 11.525281 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 0 |
| 14879 | SC | 14.0 | 191284.996653 | 4831.003347 | 60.0 | -3.0 | 0.4 | 0.5 | 38 | 1.9 | 12 | 14 | 55 | 0.4 | 12 | 1.4 | 12.186462 | 0.948683 | 5.525453 | 7.161622 | 12.161520 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 1 | 0 |
| 14880 | SC | 14.0 | 136602.302053 | 2920.697947 | 60.0 | -3.0 | 0.4 | 0.5 | 38 | 1.9 | 12 | 14 | 55 | 0.4 | 12 | 1.4 | 11.845985 | 1.549193 | 4.043051 | 6.924612 | 11.824829 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 0 |
| 14881 | SC | 11.0 | 68579.699653 | 9994.300347 | 60.0 | -3.0 | 0.4 | 0.5 | 38 | 1.9 | 12 | 14 | 55 | 0.4 | 12 | 1.4 | 11.271796 | 0.632456 | 4.709530 | 7.524561 | 11.135752 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 0 |
| 14882 | SC | 11.0 | 79991.562780 | 24316.437220 | 60.0 | -3.0 | 0.4 | 0.5 | 38 | 1.9 | 12 | 14 | 55 | 0.4 | 12 | 1.4 | 11.555103 | 1.760682 | 2.995732 | 7.524561 | 11.289676 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 0 |
| 14883 | SC | 10.0 | 70840.400546 | 6998.599454 | 60.0 | -3.0 | 0.4 | 0.5 | 38 | 1.9 | 12 | 14 | 55 | 0.4 | 12 | 1.4 | 11.262398 | 0.707107 | 6.363028 | 7.161622 | 11.168185 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 0 |
| 14884 | SC | 11.0 | 66483.015974 | 1838.984026 | 60.0 | -3.0 | 0.4 | 0.5 | 38 | 1.9 | 12 | 14 | 55 | 0.4 | 12 | 1.4 | 11.131987 | 2.529822 | 5.056246 | 7.161622 | 11.104702 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 0 |
| 14885 | SC | 14.0 | 125438.927273 | -23517.927273 | 60.0 | -3.0 | 0.4 | 0.5 | 38 | 1.9 | 12 | 14 | 55 | 0.4 | 12 | 1.4 | 11.531953 | 1.095445 | 2.079442 | 6.601230 | 11.739574 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 0 |
| 14886 | SC | 12.0 | 86109.914894 | -15917.914894 | 60.0 | -3.0 | 0.4 | 0.5 | 38 | 1.9 | 12 | 14 | 55 | 0.4 | 12 | 1.4 | 11.158990 | 0.948683 | 3.828641 | 6.663133 | 11.363380 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 0 |
| 14887 | SC | 9.0 | 55821.734185 | -5805.734185 | 60.0 | -3.0 | 0.4 | 0.5 | 38 | 1.9 | 12 | 14 | 55 | 0.4 | 12 | 1.4 | 10.820098 | 6.041523 | 4.605170 | 5.811141 | 10.929919 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 1 |
| 14888 | SC | 11.0 | 69409.643631 | -4991.643631 | 60.0 | -3.0 | 0.4 | 0.5 | 38 | 1.9 | 12 | 14 | 55 | 0.4 | 12 | 1.4 | 11.073148 | 1.897367 | 3.806662 | 7.161622 | 11.147781 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 0 |
| 14889 | SC | 12.0 | 117199.743701 | -363.743701 | 60.0 | -3.0 | 0.4 | 0.5 | 38 | 1.9 | 12 | 14 | 55 | 0.4 | 12 | 1.4 | 11.668527 | 3.114482 | 4.043051 | 6.663133 | 11.671635 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 0 |
| 14890 | SC | 15.0 | 146468.283688 | 2564.716312 | 60.0 | -3.0 | 0.4 | 0.5 | 38 | 1.9 | 12 | 14 | 55 | 0.4 | 12 | 1.4 | 11.911923 | 2.366432 | 2.397895 | 5.318120 | 11.894564 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 0 |
| 14891 | SC | 13.0 | 101243.170958 | -1894.170958 | 60.0 | -3.0 | 0.4 | 0.5 | 38 | 1.9 | 12 | 14 | 55 | 0.4 | 12 | 1.4 | 11.506394 | 2.302173 | 5.056246 | 7.172425 | 11.525281 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 0 |
| 14892 | SC | 15.0 | 149852.966062 | 5852.033938 | 60.0 | -3.0 | 0.4 | 0.5 | 38 | 1.9 | 12 | 14 | 55 | 0.4 | 12 | 1.4 | 11.955718 | 3.435113 | 4.804021 | 7.172425 | 11.917410 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 0 |
| 14893 | SC | 12.0 | 80439.445814 | 10815.554186 | 60.0 | -3.0 | 0.4 | 0.5 | 38 | 1.9 | 12 | 14 | 55 | 0.4 | 12 | 1.4 | 11.421413 | 3.301515 | 5.521461 | 6.322565 | 11.295260 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 1 |
| 14894 | SC | 11.0 | 68579.699653 | -10017.699653 | 60.0 | -3.0 | 0.4 | 0.5 | 38 | 1.9 | 12 | 14 | 55 | 0.4 | 12 | 1.4 | 10.977841 | 0.316228 | 4.709530 | 4.499810 | 11.135752 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 0 |
| 14895 | SC | 13.0 | 101243.170958 | -6647.170958 | 60.0 | -3.0 | 0.4 | 0.5 | 38 | 1.9 | 12 | 14 | 55 | 0.4 | 12 | 1.4 | 11.457370 | 2.097618 | 5.056246 | 6.274762 | 11.525281 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 0 |
| 14896 | SC | 11.0 | 69488.327856 | -5070.327856 | 60.0 | -3.0 | 0.4 | 0.5 | 38 | 1.9 | 12 | 14 | 55 | 0.4 | 12 | 1.4 | 11.073148 | 2.509980 | 4.304065 | 4.204693 | 11.148914 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 0 |
| 14897 | SC | 11.0 | 67203.914815 | -7574.914815 | 60.0 | -3.0 | 0.4 | 0.5 | 38 | 1.9 | 12 | 14 | 55 | 0.4 | 12 | 1.4 | 10.995897 | 0.894427 | 4.110874 | 6.255750 | 11.115487 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 0 |
| 14898 | SC | 11.0 | 79100.263771 | 1761.736229 | 60.0 | -3.0 | 0.4 | 0.5 | 38 | 1.9 | 12 | 14 | 55 | 0.4 | 12 | 1.4 | 11.300499 | 3.549648 | 6.363028 | 7.161622 | 11.278471 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 0 |
| 14899 | SC | 11.0 | 69858.552128 | 6272.447872 | 60.0 | -3.0 | 0.4 | 0.5 | 38 | 1.9 | 12 | 14 | 55 | 0.4 | 12 | 1.4 | 11.240211 | 2.756810 | 3.713572 | 6.011267 | 11.154228 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 0 |
| 14900 | SC | 12.0 | 85317.482394 | 13978.517606 | 60.0 | -3.0 | 0.4 | 0.5 | 38 | 1.9 | 12 | 14 | 55 | 0.4 | 12 | 1.4 | 11.505861 | 1.048809 | 4.852030 | 6.924612 | 11.354135 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 1 |
| 14901 | SC | 11.0 | 68579.699653 | -4161.699653 | 60.0 | -3.0 | 0.4 | 0.5 | 38 | 1.9 | 12 | 14 | 55 | 0.4 | 12 | 1.4 | 11.073148 | 2.366432 | 4.709530 | 7.161622 | 11.135752 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 0 |
| 14902 | SC | 11.0 | 70501.568442 | 5629.431558 | 60.0 | -3.0 | 0.4 | 0.5 | 38 | 1.9 | 12 | 14 | 55 | 0.4 | 12 | 1.4 | 11.240211 | 5.567764 | 4.852030 | 5.572154 | 11.163390 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 0 |
| 14903 | SC | 9.0 | 63564.446718 | -13548.446718 | 60.0 | -3.0 | 0.4 | 0.5 | 38 | 1.9 | 12 | 14 | 55 | 0.4 | 12 | 1.4 | 10.820098 | 1.048809 | 6.363028 | 6.322565 | 11.059810 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 0 |
| 14904 | SC | 10.0 | 67711.394247 | -10856.394247 | 60.0 | -3.0 | 0.4 | 0.5 | 38 | 1.9 | 12 | 14 | 55 | 0.4 | 12 | 1.4 | 10.948259 | 0.894427 | 3.850148 | 6.548219 | 11.123010 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 0 |
| 14905 | SC | 7.0 | 45578.500000 | 5858.500000 | 65.0 | -8.0 | 0.4 | 0.5 | 38 | 1.9 | 12 | 14 | 55 | 0.4 | 12 | 1.4 | 10.848113 | 2.738613 | 1.098612 | 7.524561 | 10.727191 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 0 |
| 14906 | SC | 12.0 | 82024.692725 | 9230.307275 | 65.0 | -8.0 | 0.4 | 0.5 | 38 | 1.9 | 12 | 14 | 55 | 0.4 | 12 | 1.4 | 11.421413 | 0.547723 | 4.709530 | 4.406719 | 11.314776 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 0 |
| 14907 | SC | 13.0 | 99688.928913 | 8818.071087 | 65.0 | -8.0 | 0.4 | 0.5 | 38 | 1.9 | 12 | 14 | 55 | 0.4 | 12 | 1.4 | 11.594570 | 4.764452 | 5.521461 | 7.161622 | 11.509810 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 0 |
| 14908 | SC | 11.0 | 63592.893443 | -5030.893443 | 65.0 | -8.0 | 0.4 | 0.5 | 38 | 1.9 | 12 | 14 | 55 | 0.4 | 12 | 1.4 | 10.977841 | 0.000000 | 2.639057 | 5.438079 | 11.060257 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 1 |
| 14909 | SC | 12.0 | 82024.692725 | -11832.692725 | 65.0 | -8.0 | 0.4 | 0.5 | 38 | 1.9 | 12 | 14 | 55 | 0.4 | 12 | 1.4 | 11.158990 | 2.144761 | 4.709530 | 5.313206 | 11.314776 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 0 |
| 14910 | SC | 15.0 | 220806.750000 | -28201.750000 | 65.0 | -8.0 | 0.4 | 0.5 | 38 | 1.9 | 12 | 14 | 55 | 0.4 | 12 | 1.4 | 12.168397 | 2.428992 | 5.525453 | 7.524561 | 12.305043 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 1 | 0 |
| 14911 | SC | 11.0 | 68579.699653 | -8065.699653 | 65.0 | -8.0 | 0.4 | 0.5 | 38 | 1.9 | 12 | 14 | 55 | 0.4 | 12 | 1.4 | 11.010630 | 1.702939 | 4.709530 | 7.524561 | 11.135752 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 1 | 0 | 0 | 1 |
| 14912 | SC | 12.0 | 92736.988124 | 6559.011876 | 65.0 | -8.0 | 0.4 | 0.5 | 38 | 1.9 | 12 | 14 | 55 | 0.4 | 12 | 1.4 | 11.505861 | 3.376389 | 6.363028 | 6.924612 | 11.437523 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 0 |
| 14913 | SC | 11.0 | 138109.540541 | 41890.459459 | 65.0 | -8.0 | 0.4 | 0.5 | 38 | 1.9 | 12 | 14 | 55 | 0.4 | 12 | 1.4 | 12.100712 | 0.316228 | 2.772589 | 6.144186 | 11.835802 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 1 |
| 14914 | SC | 11.0 | 79991.562780 | -3860.562780 | 65.0 | -8.0 | 0.4 | 0.5 | 38 | 1.9 | 12 | 14 | 55 | 0.4 | 12 | 1.4 | 11.240211 | 1.816590 | 2.995732 | 6.603944 | 11.289676 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 0 |
| 14915 | SC | 11.0 | 79100.263771 | 20195.736229 | 65.0 | -8.0 | 0.4 | 0.5 | 38 | 1.9 | 12 | 14 | 55 | 0.4 | 12 | 1.4 | 11.505861 | 1.949359 | 6.363028 | 7.172425 | 11.278471 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 0 |
| 14916 | SC | 14.0 | 123721.136031 | 2604.863969 | 65.0 | -8.0 | 0.4 | 0.5 | 38 | 1.9 | 12 | 14 | 55 | 0.4 | 12 | 1.4 | 11.746621 | 4.827007 | 5.521461 | 6.255750 | 11.725785 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 0 |
| 14917 | SC | 12.0 | 82024.692725 | -4811.692725 | 65.0 | -8.0 | 0.4 | 0.5 | 38 | 1.9 | 12 | 14 | 55 | 0.4 | 12 | 1.4 | 11.254323 | 1.732051 | 4.709530 | 6.603944 | 11.314776 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 0 |
| 14918 | SC | 13.0 | 104008.710145 | 14060.289855 | 65.0 | -8.0 | 0.4 | 0.5 | 38 | 1.9 | 12 | 14 | 55 | 0.4 | 12 | 1.4 | 11.679024 | 2.073644 | 1.791759 | 6.924612 | 11.552230 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 0 |
| 14919 | SC | 9.0 | 51761.995546 | -1745.995546 | 65.0 | -8.0 | 0.4 | 0.5 | 38 | 1.9 | 12 | 14 | 55 | 0.4 | 12 | 1.4 | 10.820098 | 1.140175 | 4.709530 | 6.322565 | 10.854411 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 0 |
14920 rows × 98 columns
Perform logistic regression with intent to extract only most important features
%%R
vars <- unlist(fullColumns)
vars <- vars[-c(94,95)]
#print(vars)
fla <- paste("SEP ~", paste(vars, collapse="+"))
fla <- as.formula(fla)
OPMAnalysisDataNoFamBinary$SEP <- as.vector(y)
BinLogit <- glm(fla, data = OPMAnalysisDataNoFamBinary, family = "binomial")
summary(BinLogit)
Call:
glm(formula = fla, family = "binomial", data = OPMAnalysisDataNoFamBinary)
Deviance Residuals:
Min 1Q Median 3Q Max
-3.0182 -0.6661 -0.1038 0.7300 3.3486
Coefficients: (8 not defined because of singularities)
Estimate Std. Error z value Pr(>|z|)
(Intercept) -3.273e+01 6.386e+00 -5.125 2.97e-07 ***
GSEGRD -5.811e-01 4.736e-02 -12.270 < 2e-16 ***
IndAvgSalary -1.229e-05 4.672e-06 -2.630 0.008545 **
SalaryOverUnderIndAvg -1.798e-06 6.633e-06 -0.271 0.786282
LowerLimitAge -5.646e-02 2.694e-02 -2.096 0.036085 *
YearsToRetirement NA NA NA NA
BLS_FEDERAL_OtherSep_Rate 5.803e+01 6.254e+00 9.279 < 2e-16 ***
BLS_FEDERAL_Quits_Rate 1.089e+02 9.746e+00 11.178 < 2e-16 ***
BLS_FEDERAL_TotalSep_Level -3.277e-01 2.734e-01 -1.198 0.230735
BLS_FEDERAL_JobOpenings_Rate -2.227e+01 5.998e+00 -3.713 0.000205 ***
BLS_FEDERAL_OtherSep_Level -1.462e+00 1.327e-01 -11.023 < 2e-16 ***
BLS_FEDERAL_Quits_Level -2.072e+00 3.386e-01 -6.118 9.49e-10 ***
BLS_FEDERAL_JobOpenings_Level 7.239e-01 2.008e-01 3.605 0.000312 ***
BLS_FEDERAL_Layoffs_Rate -6.068e+01 4.466e+00 -13.585 < 2e-16 ***
BLS_FEDERAL_Layoffs_Level 3.960e+00 3.955e-01 10.013 < 2e-16 ***
BLS_FEDERAL_TotalSep_Rate -3.096e+01 3.016e+00 -10.263 < 2e-16 ***
SALARYLog 3.755e-01 7.039e-01 0.533 0.593689
LOSSqrt -7.742e-01 2.430e-02 -31.863 < 2e-16 ***
SEPCount_EFDATE_OCCLog -5.877e-02 1.697e-02 -3.463 0.000534 ***
SEPCount_EFDATE_LOCLog -4.532e-01 1.305e-01 -3.474 0.000513 ***
IndAvgSalaryLog 4.161e+00 8.560e-01 4.861 1.17e-06 ***
AGELVL_B1 -9.866e-01 1.154e+00 -0.855 0.392526
AGELVL_C1 -3.741e-01 9.996e-01 -0.374 0.708224
AGELVL_D1 -1.535e-01 8.643e-01 -0.178 0.859011
AGELVL_E1 1.699e-01 7.306e-01 0.233 0.816125
AGELVL_F1 4.260e-01 5.976e-01 0.713 0.475969
AGELVL_G1 7.197e-01 4.653e-01 1.547 0.121961
AGELVL_H1 8.788e-01 3.344e-01 2.628 0.008586 **
AGELVL_I1 7.511e-01 2.081e-01 3.609 0.000307 ***
AGELVL_J1 NA NA NA NA
AGELVL_K1 NA NA NA NA
LOC_011 4.648e-02 4.144e-01 0.112 0.910704
LOC_021 1.706e-01 4.498e-01 0.379 0.704477
LOC_041 8.845e-01 4.196e-01 2.108 0.035044 *
LOC_051 1.275e-01 4.344e-01 0.293 0.769216
LOC_061 1.442e+00 4.987e-01 2.891 0.003839 **
LOC_081 6.195e-01 4.240e-01 1.461 0.144057
LOC_091 -4.799e-01 4.989e-01 -0.962 0.336083
LOC_101 -5.719e-01 7.195e-01 -0.795 0.426650
LOC_111 8.543e-01 4.826e-01 1.770 0.076687 .
LOC_121 7.306e-01 4.375e-01 1.670 0.094945 .
LOC_131 7.676e-01 4.281e-01 1.793 0.072989 .
LOC_151 -1.256e-02 4.057e-01 -0.031 0.975296
LOC_161 1.041e-01 4.578e-01 0.227 0.820080
LOC_171 2.223e-01 4.186e-01 0.531 0.595280
LOC_181 -4.071e-02 4.451e-01 -0.091 0.927127
LOC_191 2.682e-03 4.944e-01 0.005 0.995671
LOC_201 7.133e-01 4.503e-01 1.584 0.113183
LOC_211 1.217e-01 4.246e-01 0.287 0.774316
LOC_221 -6.172e-02 4.211e-01 -0.147 0.883470
LOC_231 3.209e-02 5.059e-01 0.063 0.949416
LOC_241 7.031e-01 4.569e-01 1.539 0.123862
LOC_251 3.305e-01 4.194e-01 0.788 0.430683
LOC_261 1.607e-01 4.227e-01 0.380 0.703828
LOC_271 5.177e-01 4.434e-01 1.167 0.243021
LOC_281 1.093e-01 4.415e-01 0.248 0.804456
LOC_291 2.000e-01 4.369e-01 0.458 0.647037
LOC_301 7.146e-01 4.403e-01 1.623 0.104571
LOC_311 -1.000e-01 4.964e-01 -0.201 0.840309
LOC_321 2.666e-01 4.461e-01 0.598 0.550173
LOC_331 -2.684e-01 6.484e-01 -0.414 0.678872
LOC_341 1.309e-03 4.192e-01 0.003 0.997509
LOC_351 7.471e-01 4.073e-01 1.834 0.066598 .
LOC_361 5.629e-01 4.389e-01 1.283 0.199644
LOC_371 5.401e-01 4.225e-01 1.278 0.201079
LOC_381 3.165e-01 5.209e-01 0.608 0.543487
LOC_391 2.746e-01 4.189e-01 0.656 0.512098
LOC_401 4.496e-01 4.136e-01 1.087 0.276969
LOC_411 4.721e-01 4.301e-01 1.097 0.272440
LOC_421 2.419e-01 4.348e-01 0.556 0.577926
LOC_441 -3.547e-01 6.673e-01 -0.531 0.595110
LOC_451 1.987e-01 4.303e-01 0.462 0.644247
LOC_461 5.412e-01 4.460e-01 1.213 0.224988
LOC_471 4.890e-02 4.352e-01 0.112 0.910533
LOC_481 1.274e+00 4.777e-01 2.667 0.007645 **
LOC_491 2.330e-01 4.340e-01 0.537 0.591254
LOC_501 -4.656e-01 7.116e-01 -0.654 0.512947
LOC_511 6.192e-01 4.712e-01 1.314 0.188832
LOC_531 9.418e-01 4.322e-01 2.179 0.029305 *
LOC_541 -9.854e-02 4.546e-01 -0.217 0.828404
LOC_551 -2.589e-01 4.460e-01 -0.581 0.561521
LOC_561 NA NA NA NA
TOA_101 -1.148e+00 2.081e-01 -5.516 3.48e-08 ***
TOA_151 -1.079e+00 2.131e-01 -5.061 4.16e-07 ***
TOA_201 1.575e-02 2.801e-01 0.056 0.955171
TOA_301 -7.135e-01 2.237e-01 -3.189 0.001426 **
TOA_321 -3.497e-01 7.998e-01 -0.437 0.661924
TOA_351 -1.503e+00 3.611e-01 -4.161 3.17e-05 ***
TOA_381 -7.468e-01 2.136e-01 -3.496 0.000472 ***
TOA_401 -1.049e+00 2.706e-01 -3.878 0.000105 ***
TOA_421 6.479e-02 4.898e-01 0.132 0.894753
TOA_441 1.318e+00 1.131e+00 1.165 0.243998
TOA_451 8.104e+00 1.392e+02 0.058 0.953584
TOA_481 NA NA NA NA
PPGROUP_111 3.799e-01 1.581e-01 2.403 0.016261 *
PPGROUP_121 NA NA NA NA
TOATYP_11 NA NA NA NA
TOATYP_21 NA NA NA NA
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
(Dispersion parameter for binomial family taken to be 1)
Null deviance: 20683 on 14919 degrees of freedom
Residual deviance: 13443 on 14830 degrees of freedom
AIC: 13623
Number of Fisher Scoring iterations: 10
%%R
alias(BinLogit)
Model :
SEP ~ GSEGRD + IndAvgSalary + SalaryOverUnderIndAvg + LowerLimitAge +
YearsToRetirement + BLS_FEDERAL_OtherSep_Rate + BLS_FEDERAL_Quits_Rate +
BLS_FEDERAL_TotalSep_Level + BLS_FEDERAL_JobOpenings_Rate +
BLS_FEDERAL_OtherSep_Level + BLS_FEDERAL_Quits_Level + BLS_FEDERAL_JobOpenings_Level +
BLS_FEDERAL_Layoffs_Rate + BLS_FEDERAL_Layoffs_Level + BLS_FEDERAL_TotalSep_Rate +
SALARYLog + LOSSqrt + SEPCount_EFDATE_OCCLog + SEPCount_EFDATE_LOCLog +
IndAvgSalaryLog + AGELVL_B + AGELVL_C + AGELVL_D + AGELVL_E +
AGELVL_F + AGELVL_G + AGELVL_H + AGELVL_I + AGELVL_J + AGELVL_K +
LOC_01 + LOC_02 + LOC_04 + LOC_05 + LOC_06 + LOC_08 + LOC_09 +
LOC_10 + LOC_11 + LOC_12 + LOC_13 + LOC_15 + LOC_16 + LOC_17 +
LOC_18 + LOC_19 + LOC_20 + LOC_21 + LOC_22 + LOC_23 + LOC_24 +
LOC_25 + LOC_26 + LOC_27 + LOC_28 + LOC_29 + LOC_30 + LOC_31 +
LOC_32 + LOC_33 + LOC_34 + LOC_35 + LOC_36 + LOC_37 + LOC_38 +
LOC_39 + LOC_40 + LOC_41 + LOC_42 + LOC_44 + LOC_45 + LOC_46 +
LOC_47 + LOC_48 + LOC_49 + LOC_50 + LOC_51 + LOC_53 + LOC_54 +
LOC_55 + LOC_56 + TOA_10 + TOA_15 + TOA_20 + TOA_30 + TOA_32 +
TOA_35 + TOA_38 + TOA_40 + TOA_42 + TOA_44 + TOA_45 + TOA_48 +
PPGROUP_11 + PPGROUP_12 + TOATYP_1 + TOATYP_2
Complete :
(Intercept) GSEGRD IndAvgSalary SalaryOverUnderIndAvg
YearsToRetirement 57 0 0 0
AGELVL_J1 13 0 0 0
AGELVL_K1 -12 0 0 0
LOC_561 1 0 0 0
TOA_481 1 0 0 0
PPGROUP_121 1 0 0 0
TOATYP_11 0 0 0 0
TOATYP_21 1 0 0 0
LowerLimitAge BLS_FEDERAL_OtherSep_Rate
YearsToRetirement -1 0
AGELVL_J1 -1/5 0
AGELVL_K1 1/5 0
LOC_561 0 0
TOA_481 0 0
PPGROUP_121 0 0
TOATYP_11 0 0
TOATYP_21 0 0
BLS_FEDERAL_Quits_Rate BLS_FEDERAL_TotalSep_Level
YearsToRetirement 0 0
AGELVL_J1 0 0
AGELVL_K1 0 0
LOC_561 0 0
TOA_481 0 0
PPGROUP_121 0 0
TOATYP_11 0 0
TOATYP_21 0 0
BLS_FEDERAL_JobOpenings_Rate BLS_FEDERAL_OtherSep_Level
YearsToRetirement 0 0
AGELVL_J1 0 0
AGELVL_K1 0 0
LOC_561 0 0
TOA_481 0 0
PPGROUP_121 0 0
TOATYP_11 0 0
TOATYP_21 0 0
BLS_FEDERAL_Quits_Level BLS_FEDERAL_JobOpenings_Level
YearsToRetirement 0 0
AGELVL_J1 0 0
AGELVL_K1 0 0
LOC_561 0 0
TOA_481 0 0
PPGROUP_121 0 0
TOATYP_11 0 0
TOATYP_21 0 0
BLS_FEDERAL_Layoffs_Rate BLS_FEDERAL_Layoffs_Level
YearsToRetirement 0 0
AGELVL_J1 0 0
AGELVL_K1 0 0
LOC_561 0 0
TOA_481 0 0
PPGROUP_121 0 0
TOATYP_11 0 0
TOATYP_21 0 0
BLS_FEDERAL_TotalSep_Rate SALARYLog LOSSqrt
YearsToRetirement 0 0 0
AGELVL_J1 0 0 0
AGELVL_K1 0 0 0
LOC_561 0 0 0
TOA_481 0 0 0
PPGROUP_121 0 0 0
TOATYP_11 0 0 0
TOATYP_21 0 0 0
SEPCount_EFDATE_OCCLog SEPCount_EFDATE_LOCLog IndAvgSalaryLog
YearsToRetirement 0 0 0
AGELVL_J1 0 0 0
AGELVL_K1 0 0 0
LOC_561 0 0 0
TOA_481 0 0 0
PPGROUP_121 0 0 0
TOATYP_11 0 0 0
TOATYP_21 0 0 0
AGELVL_B1 AGELVL_C1 AGELVL_D1 AGELVL_E1 AGELVL_F1 AGELVL_G1
YearsToRetirement 0 0 0 0 0 0
AGELVL_J1 -9 -8 -7 -6 -5 -4
AGELVL_K1 8 7 6 5 4 3
LOC_561 0 0 0 0 0 0
TOA_481 0 0 0 0 0 0
PPGROUP_121 0 0 0 0 0 0
TOATYP_11 0 0 0 0 0 0
TOATYP_21 0 0 0 0 0 0
AGELVL_H1 AGELVL_I1 LOC_011 LOC_021 LOC_041 LOC_051 LOC_061
YearsToRetirement 0 0 0 0 0 0 0
AGELVL_J1 -3 -2 0 0 0 0 0
AGELVL_K1 2 1 0 0 0 0 0
LOC_561 0 0 -1 -1 -1 -1 -1
TOA_481 0 0 0 0 0 0 0
PPGROUP_121 0 0 0 0 0 0 0
TOATYP_11 0 0 0 0 0 0 0
TOATYP_21 0 0 0 0 0 0 0
LOC_081 LOC_091 LOC_101 LOC_111 LOC_121 LOC_131 LOC_151
YearsToRetirement 0 0 0 0 0 0 0
AGELVL_J1 0 0 0 0 0 0 0
AGELVL_K1 0 0 0 0 0 0 0
LOC_561 -1 -1 -1 -1 -1 -1 -1
TOA_481 0 0 0 0 0 0 0
PPGROUP_121 0 0 0 0 0 0 0
TOATYP_11 0 0 0 0 0 0 0
TOATYP_21 0 0 0 0 0 0 0
LOC_161 LOC_171 LOC_181 LOC_191 LOC_201 LOC_211 LOC_221
YearsToRetirement 0 0 0 0 0 0 0
AGELVL_J1 0 0 0 0 0 0 0
AGELVL_K1 0 0 0 0 0 0 0
LOC_561 -1 -1 -1 -1 -1 -1 -1
TOA_481 0 0 0 0 0 0 0
PPGROUP_121 0 0 0 0 0 0 0
TOATYP_11 0 0 0 0 0 0 0
TOATYP_21 0 0 0 0 0 0 0
LOC_231 LOC_241 LOC_251 LOC_261 LOC_271 LOC_281 LOC_291
YearsToRetirement 0 0 0 0 0 0 0
AGELVL_J1 0 0 0 0 0 0 0
AGELVL_K1 0 0 0 0 0 0 0
LOC_561 -1 -1 -1 -1 -1 -1 -1
TOA_481 0 0 0 0 0 0 0
PPGROUP_121 0 0 0 0 0 0 0
TOATYP_11 0 0 0 0 0 0 0
TOATYP_21 0 0 0 0 0 0 0
LOC_301 LOC_311 LOC_321 LOC_331 LOC_341 LOC_351 LOC_361
YearsToRetirement 0 0 0 0 0 0 0
AGELVL_J1 0 0 0 0 0 0 0
AGELVL_K1 0 0 0 0 0 0 0
LOC_561 -1 -1 -1 -1 -1 -1 -1
TOA_481 0 0 0 0 0 0 0
PPGROUP_121 0 0 0 0 0 0 0
TOATYP_11 0 0 0 0 0 0 0
TOATYP_21 0 0 0 0 0 0 0
LOC_371 LOC_381 LOC_391 LOC_401 LOC_411 LOC_421 LOC_441
YearsToRetirement 0 0 0 0 0 0 0
AGELVL_J1 0 0 0 0 0 0 0
AGELVL_K1 0 0 0 0 0 0 0
LOC_561 -1 -1 -1 -1 -1 -1 -1
TOA_481 0 0 0 0 0 0 0
PPGROUP_121 0 0 0 0 0 0 0
TOATYP_11 0 0 0 0 0 0 0
TOATYP_21 0 0 0 0 0 0 0
LOC_451 LOC_461 LOC_471 LOC_481 LOC_491 LOC_501 LOC_511
YearsToRetirement 0 0 0 0 0 0 0
AGELVL_J1 0 0 0 0 0 0 0
AGELVL_K1 0 0 0 0 0 0 0
LOC_561 -1 -1 -1 -1 -1 -1 -1
TOA_481 0 0 0 0 0 0 0
PPGROUP_121 0 0 0 0 0 0 0
TOATYP_11 0 0 0 0 0 0 0
TOATYP_21 0 0 0 0 0 0 0
LOC_531 LOC_541 LOC_551 TOA_101 TOA_151 TOA_201 TOA_301
YearsToRetirement 0 0 0 0 0 0 0
AGELVL_J1 0 0 0 0 0 0 0
AGELVL_K1 0 0 0 0 0 0 0
LOC_561 -1 -1 -1 0 0 0 0
TOA_481 0 0 0 -1 -1 -1 -1
PPGROUP_121 0 0 0 0 0 0 0
TOATYP_11 0 0 0 1 1 0 1
TOATYP_21 0 0 0 -1 -1 0 -1
TOA_321 TOA_351 TOA_381 TOA_401 TOA_421 TOA_441 TOA_451
YearsToRetirement 0 0 0 0 0 0 0
AGELVL_J1 0 0 0 0 0 0 0
AGELVL_K1 0 0 0 0 0 0 0
LOC_561 0 0 0 0 0 0 0
TOA_481 -1 -1 -1 -1 -1 -1 -1
PPGROUP_121 0 0 0 0 0 0 0
TOATYP_11 1 1 1 0 0 0 0
TOATYP_21 -1 -1 -1 0 0 0 0
PPGROUP_111
YearsToRetirement 0
AGELVL_J1 0
AGELVL_K1 0
LOC_561 0
TOA_481 0
PPGROUP_121 -1
TOATYP_11 0
TOATYP_21 0
%%R
tmp <- alias(BinLogit)$Complete
print(attributes(tmp))
aliased <- dimnames(tmp)[[1]]
$dim [1] 8 90 $dimnames $dimnames[[1]] [1] "YearsToRetirement" "AGELVL_J1" "AGELVL_K1" [4] "LOC_561" "TOA_481" "PPGROUP_121" [7] "TOATYP_11" "TOATYP_21" $dimnames[[2]] [1] "(Intercept)" "GSEGRD" [3] "IndAvgSalary" "SalaryOverUnderIndAvg" [5] "LowerLimitAge" "BLS_FEDERAL_OtherSep_Rate" [7] "BLS_FEDERAL_Quits_Rate" "BLS_FEDERAL_TotalSep_Level" [9] "BLS_FEDERAL_JobOpenings_Rate" "BLS_FEDERAL_OtherSep_Level" [11] "BLS_FEDERAL_Quits_Level" "BLS_FEDERAL_JobOpenings_Level" [13] "BLS_FEDERAL_Layoffs_Rate" "BLS_FEDERAL_Layoffs_Level" [15] "BLS_FEDERAL_TotalSep_Rate" "SALARYLog" [17] "LOSSqrt" "SEPCount_EFDATE_OCCLog" [19] "SEPCount_EFDATE_LOCLog" "IndAvgSalaryLog" [21] "AGELVL_B1" "AGELVL_C1" [23] "AGELVL_D1" "AGELVL_E1" [25] "AGELVL_F1" "AGELVL_G1" [27] "AGELVL_H1" "AGELVL_I1" [29] "LOC_011" "LOC_021" [31] "LOC_041" "LOC_051" [33] "LOC_061" "LOC_081" [35] "LOC_091" "LOC_101" [37] "LOC_111" "LOC_121" [39] "LOC_131" "LOC_151" [41] "LOC_161" "LOC_171" [43] "LOC_181" "LOC_191" [45] "LOC_201" "LOC_211" [47] "LOC_221" "LOC_231" [49] "LOC_241" "LOC_251" [51] "LOC_261" "LOC_271" [53] "LOC_281" "LOC_291" [55] "LOC_301" "LOC_311" [57] "LOC_321" "LOC_331" [59] "LOC_341" "LOC_351" [61] "LOC_361" "LOC_371" [63] "LOC_381" "LOC_391" [65] "LOC_401" "LOC_411" [67] "LOC_421" "LOC_441" [69] "LOC_451" "LOC_461" [71] "LOC_471" "LOC_481" [73] "LOC_491" "LOC_501" [75] "LOC_511" "LOC_531" [77] "LOC_541" "LOC_551" [79] "TOA_101" "TOA_151" [81] "TOA_201" "TOA_301" [83] "TOA_321" "TOA_351" [85] "TOA_381" "TOA_401" [87] "TOA_421" "TOA_441" [89] "TOA_451" "PPGROUP_111" $fracs [1] "57" "13" "-12" "1" "1" "1" "0" "1" "0" "0" [11] "0" "0" "0" "0" "0" "0" "0" "0" "0" "0" [21] "0" "0" "0" "0" "0" "0" "0" "0" "0" "0" [31] "0" "0" "-1" "-1/5" "1/5" "0" "0" "0" "0" "0" [41] "0" "0" "0" "0" "0" "0" "0" "0" "0" "0" [51] "0" "0" "0" "0" "0" "0" "0" "0" "0" "0" [61] "0" "0" "0" "0" "0" "0" "0" "0" "0" "0" [71] "0" "0" "0" "0" "0" "0" "0" "0" "0" "0" [81] "0" "0" "0" "0" "0" "0" "0" "0" "0" "0" [91] "0" "0" "0" "0" "0" "0" "0" "0" "0" "0" [101] "0" "0" "0" "0" "0" "0" "0" "0" "0" "0" [111] "0" "0" "0" "0" "0" "0" "0" "0" "0" "0" [121] "0" "0" "0" "0" "0" "0" "0" "0" "0" "0" [131] "0" "0" "0" "0" "0" "0" "0" "0" "0" "0" [141] "0" "0" "0" "0" "0" "0" "0" "0" "0" "0" [151] "0" "0" "0" "0" "0" "0" "0" "0" "0" "0" [161] "0" "-9" "8" "0" "0" "0" "0" "0" "0" "-8" [171] "7" "0" "0" "0" "0" "0" "0" "-7" "6" "0" [181] "0" "0" "0" "0" "0" "-6" "5" "0" "0" "0" [191] "0" "0" "0" "-5" "4" "0" "0" "0" "0" "0" [201] "0" "-4" "3" "0" "0" "0" "0" "0" "0" "-3" [211] "2" "0" "0" "0" "0" "0" "0" "-2" "1" "0" [221] "0" "0" "0" "0" "0" "0" "0" "-1" "0" "0" [231] "0" "0" "0" "0" "0" "-1" "0" "0" "0" "0" [241] "0" "0" "0" "-1" "0" "0" "0" "0" "0" "0" [251] "0" "-1" "0" "0" "0" "0" "0" "0" "0" "-1" [261] "0" "0" "0" "0" "0" "0" "0" "-1" "0" "0" [271] "0" "0" "0" "0" "0" "-1" "0" "0" "0" "0" [281] "0" "0" "0" "-1" "0" "0" "0" "0" "0" "0" [291] "0" "-1" "0" "0" "0" "0" "0" "0" "0" "-1" [301] "0" "0" "0" "0" "0" "0" "0" "-1" "0" "0" [311] "0" "0" "0" "0" "0" "-1" "0" "0" "0" "0" [321] "0" "0" "0" "-1" "0" "0" "0" "0" "0" "0" [331] "0" "-1" "0" "0" "0" "0" "0" "0" "0" "-1" [341] "0" "0" "0" "0" "0" "0" "0" "-1" "0" "0" [351] "0" "0" "0" "0" "0" "-1" "0" "0" "0" "0" [361] "0" "0" "0" "-1" "0" "0" "0" "0" "0" "0" [371] "0" "-1" "0" "0" "0" "0" "0" "0" "0" "-1" [381] "0" "0" "0" "0" "0" "0" "0" "-1" "0" "0" [391] "0" "0" "0" "0" "0" "-1" "0" "0" "0" "0" [401] "0" "0" "0" "-1" "0" "0" "0" "0" "0" "0" [411] "0" "-1" "0" "0" "0" "0" "0" "0" "0" "-1" [421] "0" "0" "0" "0" "0" "0" "0" "-1" "0" "0" [431] "0" "0" "0" "0" "0" "-1" "0" "0" "0" "0" [441] "0" "0" "0" "-1" "0" "0" "0" "0" "0" "0" [451] "0" "-1" "0" "0" "0" "0" "0" "0" "0" "-1" [461] "0" "0" "0" "0" "0" "0" "0" "-1" "0" "0" [471] "0" "0" "0" "0" "0" "-1" "0" "0" "0" "0" [481] "0" "0" "0" "-1" "0" "0" "0" "0" "0" "0" [491] "0" "-1" "0" "0" "0" "0" "0" "0" "0" "-1" [501] "0" "0" "0" "0" "0" "0" "0" "-1" "0" "0" [511] "0" "0" "0" "0" "0" "-1" "0" "0" "0" "0" [521] "0" "0" "0" "-1" "0" "0" "0" "0" "0" "0" [531] "0" "-1" "0" "0" "0" "0" "0" "0" "0" "-1" [541] "0" "0" "0" "0" "0" "0" "0" "-1" "0" "0" [551] "0" "0" "0" "0" "0" "-1" "0" "0" "0" "0" [561] "0" "0" "0" "-1" "0" "0" "0" "0" "0" "0" [571] "0" "-1" "0" "0" "0" "0" "0" "0" "0" "-1" [581] "0" "0" "0" "0" "0" "0" "0" "-1" "0" "0" [591] "0" "0" "0" "0" "0" "-1" "0" "0" "0" "0" [601] "0" "0" "0" "-1" "0" "0" "0" "0" "0" "0" [611] "0" "-1" "0" "0" "0" "0" "0" "0" "0" "-1" [621] "0" "0" "0" "0" "0" "0" "0" "0" "-1" "0" [631] "1" "-1" "0" "0" "0" "0" "-1" "0" "1" "-1" [641] "0" "0" "0" "0" "-1" "0" "0" "0" "0" "0" [651] "0" "0" "-1" "0" "1" "-1" "0" "0" "0" "0" [661] "-1" "0" "1" "-1" "0" "0" "0" "0" "-1" "0" [671] "1" "-1" "0" "0" "0" "0" "-1" "0" "1" "-1" [681] "0" "0" "0" "0" "-1" "0" "0" "0" "0" "0" [691] "0" "0" "-1" "0" "0" "0" "0" "0" "0" "0" [701] "-1" "0" "0" "0" "0" "0" "0" "0" "-1" "0" [711] "0" "0" "0" "0" "0" "0" "0" "-1" "0" "0" $class [1] "fractions" "matrix"
%%R
aliased <- ifelse(grepl('[[:digit:]]$', aliased), substr(aliased, 1, nchar(aliased)-1), aliased)
print(c("Following attributes will be dropped from model due to multicollinearity:", aliased))
paste(as.character(length(vars) - length(vars[!vars %in% c(aliased)])), "attributes removed from model input")
[1] "Following attributes will be dropped from model due to multicollinearity:" [2] "YearsToRetirement" [3] "AGELVL_J" [4] "AGELVL_K" [5] "LOC_56" [6] "TOA_48" [7] "PPGROUP_12" [8] "TOATYP_1" [9] "TOATYP_2" [1] "8 attributes removed from model input"
%%R
runLogit <- function(less, vars){
fla <- paste("SEP ~", paste(vars, collapse="+"))
fla <- as.formula(fla)
binLog <- glm(fla, data = OPMAnalysisDataNoFamBinary, family = "binomial")
return(binLog)
}
vars <- vars[!(vars %in% c(aliased))]
BinLogit2 <- runLogit(aliased, vars)
summary(BinLogit2)
Call:
glm(formula = fla, family = "binomial", data = OPMAnalysisDataNoFamBinary)
Deviance Residuals:
Min 1Q Median 3Q Max
-3.0182 -0.6661 -0.1038 0.7300 3.3486
Coefficients:
Estimate Std. Error z value Pr(>|z|)
(Intercept) -3.273e+01 6.386e+00 -5.125 2.97e-07 ***
GSEGRD -5.811e-01 4.736e-02 -12.270 < 2e-16 ***
IndAvgSalary -1.229e-05 4.672e-06 -2.630 0.008545 **
SalaryOverUnderIndAvg -1.798e-06 6.633e-06 -0.271 0.786282
LowerLimitAge -5.646e-02 2.694e-02 -2.096 0.036085 *
BLS_FEDERAL_OtherSep_Rate 5.803e+01 6.254e+00 9.279 < 2e-16 ***
BLS_FEDERAL_Quits_Rate 1.089e+02 9.746e+00 11.178 < 2e-16 ***
BLS_FEDERAL_TotalSep_Level -3.277e-01 2.734e-01 -1.198 0.230735
BLS_FEDERAL_JobOpenings_Rate -2.227e+01 5.998e+00 -3.713 0.000205 ***
BLS_FEDERAL_OtherSep_Level -1.462e+00 1.327e-01 -11.023 < 2e-16 ***
BLS_FEDERAL_Quits_Level -2.072e+00 3.386e-01 -6.118 9.49e-10 ***
BLS_FEDERAL_JobOpenings_Level 7.239e-01 2.008e-01 3.605 0.000312 ***
BLS_FEDERAL_Layoffs_Rate -6.068e+01 4.466e+00 -13.585 < 2e-16 ***
BLS_FEDERAL_Layoffs_Level 3.960e+00 3.955e-01 10.013 < 2e-16 ***
BLS_FEDERAL_TotalSep_Rate -3.096e+01 3.016e+00 -10.263 < 2e-16 ***
SALARYLog 3.755e-01 7.039e-01 0.533 0.593689
LOSSqrt -7.742e-01 2.430e-02 -31.863 < 2e-16 ***
SEPCount_EFDATE_OCCLog -5.877e-02 1.697e-02 -3.463 0.000534 ***
SEPCount_EFDATE_LOCLog -4.532e-01 1.305e-01 -3.474 0.000513 ***
IndAvgSalaryLog 4.161e+00 8.560e-01 4.861 1.17e-06 ***
AGELVL_B1 -9.866e-01 1.154e+00 -0.855 0.392526
AGELVL_C1 -3.741e-01 9.996e-01 -0.374 0.708224
AGELVL_D1 -1.535e-01 8.643e-01 -0.178 0.859011
AGELVL_E1 1.699e-01 7.306e-01 0.233 0.816125
AGELVL_F1 4.260e-01 5.976e-01 0.713 0.475969
AGELVL_G1 7.197e-01 4.653e-01 1.547 0.121961
AGELVL_H1 8.788e-01 3.344e-01 2.628 0.008586 **
AGELVL_I1 7.511e-01 2.081e-01 3.609 0.000307 ***
LOC_011 4.648e-02 4.144e-01 0.112 0.910704
LOC_021 1.706e-01 4.498e-01 0.379 0.704477
LOC_041 8.845e-01 4.196e-01 2.108 0.035044 *
LOC_051 1.275e-01 4.344e-01 0.293 0.769216
LOC_061 1.442e+00 4.987e-01 2.891 0.003839 **
LOC_081 6.195e-01 4.240e-01 1.461 0.144057
LOC_091 -4.799e-01 4.989e-01 -0.962 0.336083
LOC_101 -5.719e-01 7.195e-01 -0.795 0.426650
LOC_111 8.543e-01 4.826e-01 1.770 0.076687 .
LOC_121 7.306e-01 4.375e-01 1.670 0.094945 .
LOC_131 7.676e-01 4.281e-01 1.793 0.072989 .
LOC_151 -1.256e-02 4.057e-01 -0.031 0.975296
LOC_161 1.041e-01 4.578e-01 0.227 0.820080
LOC_171 2.223e-01 4.186e-01 0.531 0.595280
LOC_181 -4.071e-02 4.451e-01 -0.091 0.927127
LOC_191 2.682e-03 4.944e-01 0.005 0.995671
LOC_201 7.133e-01 4.503e-01 1.584 0.113183
LOC_211 1.217e-01 4.246e-01 0.287 0.774316
LOC_221 -6.172e-02 4.211e-01 -0.147 0.883470
LOC_231 3.209e-02 5.059e-01 0.063 0.949416
LOC_241 7.031e-01 4.569e-01 1.539 0.123862
LOC_251 3.305e-01 4.194e-01 0.788 0.430683
LOC_261 1.607e-01 4.227e-01 0.380 0.703828
LOC_271 5.177e-01 4.434e-01 1.167 0.243021
LOC_281 1.093e-01 4.415e-01 0.248 0.804456
LOC_291 2.000e-01 4.369e-01 0.458 0.647037
LOC_301 7.146e-01 4.403e-01 1.623 0.104571
LOC_311 -1.000e-01 4.964e-01 -0.201 0.840309
LOC_321 2.666e-01 4.461e-01 0.598 0.550173
LOC_331 -2.684e-01 6.484e-01 -0.414 0.678872
LOC_341 1.309e-03 4.192e-01 0.003 0.997509
LOC_351 7.471e-01 4.073e-01 1.834 0.066598 .
LOC_361 5.629e-01 4.389e-01 1.283 0.199644
LOC_371 5.401e-01 4.225e-01 1.278 0.201079
LOC_381 3.165e-01 5.209e-01 0.608 0.543487
LOC_391 2.746e-01 4.189e-01 0.656 0.512098
LOC_401 4.496e-01 4.136e-01 1.087 0.276969
LOC_411 4.721e-01 4.301e-01 1.097 0.272440
LOC_421 2.419e-01 4.348e-01 0.556 0.577926
LOC_441 -3.547e-01 6.673e-01 -0.531 0.595110
LOC_451 1.987e-01 4.303e-01 0.462 0.644247
LOC_461 5.412e-01 4.460e-01 1.213 0.224988
LOC_471 4.890e-02 4.352e-01 0.112 0.910533
LOC_481 1.274e+00 4.777e-01 2.667 0.007645 **
LOC_491 2.330e-01 4.340e-01 0.537 0.591254
LOC_501 -4.656e-01 7.116e-01 -0.654 0.512947
LOC_511 6.192e-01 4.712e-01 1.314 0.188832
LOC_531 9.418e-01 4.322e-01 2.179 0.029305 *
LOC_541 -9.854e-02 4.546e-01 -0.217 0.828404
LOC_551 -2.589e-01 4.460e-01 -0.581 0.561521
TOA_101 -1.148e+00 2.081e-01 -5.516 3.48e-08 ***
TOA_151 -1.079e+00 2.131e-01 -5.061 4.16e-07 ***
TOA_201 1.575e-02 2.801e-01 0.056 0.955171
TOA_301 -7.135e-01 2.237e-01 -3.189 0.001426 **
TOA_321 -3.497e-01 7.998e-01 -0.437 0.661924
TOA_351 -1.503e+00 3.611e-01 -4.161 3.17e-05 ***
TOA_381 -7.468e-01 2.136e-01 -3.496 0.000472 ***
TOA_401 -1.049e+00 2.706e-01 -3.878 0.000105 ***
TOA_421 6.479e-02 4.898e-01 0.132 0.894753
TOA_441 1.318e+00 1.131e+00 1.165 0.243998
TOA_451 8.104e+00 1.392e+02 0.058 0.953584
PPGROUP_111 3.799e-01 1.581e-01 2.403 0.016261 *
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
(Dispersion parameter for binomial family taken to be 1)
Null deviance: 20683 on 14919 degrees of freedom
Residual deviance: 13443 on 14830 degrees of freedom
AIC: 13623
Number of Fisher Scoring iterations: 10
%%R
#install.packages("car")
#require(car)
library(car)
runVifs <- function(logit){
tmp <- as.data.frame(vif(logit))
colnames(tmp) <- "VIF"
scipen.default <- getOption("scipen")
options(scipen=999)
print(tmp)
options(scipen=scipen.default)
return(tmp)
}
vifs.BinLogit2 <- runVifs(BinLogit2)
VIF GSEGRD 14.110175 IndAvgSalary 42.900149 SalaryOverUnderIndAvg 9.579701 LowerLimitAge 219.325838 BLS_FEDERAL_OtherSep_Rate 564.651743 BLS_FEDERAL_Quits_Rate 1052.281028 BLS_FEDERAL_TotalSep_Level 10461.617797 BLS_FEDERAL_JobOpenings_Rate 12261.146763 BLS_FEDERAL_OtherSep_Level 207.403680 BLS_FEDERAL_Quits_Level 1106.678602 BLS_FEDERAL_JobOpenings_Level 11662.910265 BLS_FEDERAL_Layoffs_Rate 1769.294407 BLS_FEDERAL_Layoffs_Level 10430.725186 BLS_FEDERAL_TotalSep_Rate 1755.735030 SALARYLog 102.628189 LOSSqrt 1.973772 SEPCount_EFDATE_OCCLog 1.123983 SEPCount_EFDATE_LOCLog 24.988435 IndAvgSalaryLog 142.077732 AGELVL_B 27.744404 AGELVL_C 207.457982 AGELVL_D 237.573778 AGELVL_E 149.529939 AGELVL_F 83.990092 AGELVL_G 45.565410 AGELVL_H 23.764489 AGELVL_I 9.371740 LOC_01 6.040791 LOC_02 2.917066 LOC_04 9.579683 LOC_05 3.215149 LOC_06 37.535884 LOC_08 11.672734 LOC_09 2.212192 LOC_10 1.396902 LOC_11 53.702314 LOC_12 14.250681 LOC_13 13.959157 LOC_15 6.228701 LOC_16 2.652246 LOC_17 8.129706 LOC_18 3.089854 LOC_19 2.145980 LOC_20 2.848711 LOC_21 4.170515 LOC_22 3.983023 LOC_23 2.097936 LOC_24 35.724766 LOC_25 4.990639 LOC_26 4.449805 LOC_27 3.154766 LOC_28 3.109605 LOC_29 6.115997 LOC_30 3.273054 LOC_31 2.105127 LOC_32 2.898692 LOC_33 1.515369 LOC_34 4.399908 LOC_35 7.413789 LOC_36 9.518652 LOC_37 7.692933 LOC_38 1.947582 LOC_39 8.816013 LOC_40 6.097166 LOC_41 4.719139 LOC_42 9.197718 LOC_44 1.479747 LOC_45 3.624529 LOC_46 2.879513 LOC_47 4.013711 LOC_48 29.433241 LOC_49 4.168211 LOC_50 1.409060 LOC_51 41.609055 LOC_53 11.631298 LOC_54 2.701110 LOC_55 2.991687 TOA_10 22.507735 TOA_15 12.146029 TOA_20 2.121598 TOA_30 7.378587 TOA_32 1.077935 TOA_35 1.553062 TOA_38 10.937132 TOA_40 2.369396 TOA_42 1.216934 TOA_44 1.039689 TOA_45 1.000003 PPGROUP_11 1.895725
%%R
vifs.BinLogitRepeat <- vifs.BinLogit2
vars.Repeat <- vars
vif.removed <- vector(mode="character", length=0)
ndev.vect <- vector(mode="character", length=0)
ndf.vect <- vector(mode="character", length=0)
pchisq.vect <- vector(mode="character", length=0)
logLik.vect <- vector(mode="character", length=0)
AIC.vect <- vector(mode="character", length=0)
BIC.vect <- vector(mode="character", length=0)
for(i in seq(1,12)){
remove <- rownames(vifs.BinLogitRepeat)[which.max(vifs.BinLogitRepeat$VIF)]
vif.removed <- c(vif.removed, remove)
cat("\n\n\nRemoved BEFORE this step:", remove, "\n")
vars.Repeat <- vars.Repeat[!(vars.Repeat %in% c(remove))]
BinLogitRepeat <- runLogit(remove, vars.Repeat)
print(summary(BinLogitRepeat))
vifs.BinLogitRepeat <- runVifs(BinLogitRepeat)
##goodness of fit
ndev.vect <- c(ndev.vect, with(BinLogitRepeat, null.deviance - deviance))
ndf.vect <- c(ndf.vect, with(BinLogitRepeat, df.null - df.residual))
pchisq.vect <- c(pchisq.vect, with(BinLogitRepeat, pchisq(null.deviance - deviance, df.null - df.residual, lower.tail = FALSE)))
logLik.vect <- c(logLik.vect, logLik(BinLogitRepeat))
AIC.vect <- c(AIC.vect, AIC(BinLogitRepeat))
BIC.vect <- c(BIC.vect, BIC(BinLogitRepeat))
}
cat("\nFollowing variables removed based on VIF values (in order of removal):\n")
print(vif.removed)
cat("\n\nNull Deviances (in order):\n")
print(ndev.vect)
cat("\nMin value at iteration = ", which.min(ndev.vect))
cat("\n\nDiff Degrees of Freedom (in order):\n")
print(ndf.vect)
cat("\nMin value at iteration = ", which.min(ndf.vect))
#cat("\n\nP-ChiSquare (in order):\n")
#print(pchisq.vect)
#cat("\nMin value at iteration = ", which.min(pchisq.vect))
cat("\n\nLog Likelihoods (in order):\n")
print(logLik.vect)
cat("\nMin value at iteration = ", which.min(logLik.vect))
cat("\n\nAIC values (in order):\n")
print(AIC.vect)
cat("\nMin value at iteration = ", which.min(AIC.vect))
cat("\n\nBIC values (in order):\n")
print(BIC.vect)
cat("\nMin value at iteration = ", which.min(BIC.vect))
Removed BEFORE this step: BLS_FEDERAL_JobOpenings_Rate
Call:
glm(formula = fla, family = "binomial", data = OPMAnalysisDataNoFamBinary)
Deviance Residuals:
Min 1Q Median 3Q Max
-3.0254 -0.6709 -0.1042 0.7323 3.3514
Coefficients:
Estimate Std. Error z value Pr(>|z|)
(Intercept) -3.682e+01 6.287e+00 -5.857 4.71e-09 ***
GSEGRD -5.812e-01 4.733e-02 -12.279 < 2e-16 ***
IndAvgSalary -1.243e-05 4.668e-06 -2.662 0.007770 **
SalaryOverUnderIndAvg -1.955e-06 6.634e-06 -0.295 0.768161
LowerLimitAge -5.608e-02 2.693e-02 -2.083 0.037275 *
BLS_FEDERAL_OtherSep_Rate 3.549e+01 1.457e+00 24.360 < 2e-16 ***
BLS_FEDERAL_Quits_Rate 8.808e+01 7.932e+00 11.105 < 2e-16 ***
BLS_FEDERAL_TotalSep_Level 4.514e-01 1.749e-01 2.581 0.009850 **
BLS_FEDERAL_OtherSep_Level -1.499e+00 1.323e-01 -11.330 < 2e-16 ***
BLS_FEDERAL_Quits_Level -2.276e+00 3.340e-01 -6.814 9.52e-12 ***
BLS_FEDERAL_JobOpenings_Level -2.126e-02 6.286e-03 -3.382 0.000721 ***
BLS_FEDERAL_Layoffs_Rate -5.276e+01 3.913e+00 -13.484 < 2e-16 ***
BLS_FEDERAL_Layoffs_Level 2.569e+00 1.237e-01 20.777 < 2e-16 ***
BLS_FEDERAL_TotalSep_Rate -2.419e+01 2.410e+00 -10.039 < 2e-16 ***
SALARYLog 3.867e-01 7.038e-01 0.550 0.582662
LOSSqrt -7.741e-01 2.429e-02 -31.871 < 2e-16 ***
SEPCount_EFDATE_OCCLog -5.978e-02 1.696e-02 -3.525 0.000424 ***
SEPCount_EFDATE_LOCLog -4.800e-01 1.301e-01 -3.689 0.000225 ***
IndAvgSalaryLog 4.157e+00 8.554e-01 4.860 1.17e-06 ***
AGELVL_B1 -9.663e-01 1.153e+00 -0.838 0.402062
AGELVL_C1 -3.577e-01 9.990e-01 -0.358 0.720271
AGELVL_D1 -1.396e-01 8.638e-01 -0.162 0.871624
AGELVL_E1 1.813e-01 7.302e-01 0.248 0.803925
AGELVL_F1 4.349e-01 5.973e-01 0.728 0.466540
AGELVL_G1 7.293e-01 4.650e-01 1.568 0.116798
AGELVL_H1 8.853e-01 3.341e-01 2.649 0.008064 **
AGELVL_I1 7.532e-01 2.079e-01 3.622 0.000292 ***
LOC_011 7.560e-02 4.148e-01 0.182 0.855401
LOC_021 1.881e-01 4.500e-01 0.418 0.675928
LOC_041 9.319e-01 4.198e-01 2.220 0.026421 *
LOC_051 1.276e-01 4.352e-01 0.293 0.769306
LOC_061 1.519e+00 4.982e-01 3.048 0.002303 **
LOC_081 6.686e-01 4.242e-01 1.576 0.115006
LOC_091 -4.660e-01 4.995e-01 -0.933 0.350843
LOC_101 -6.256e-01 7.209e-01 -0.868 0.385444
LOC_111 9.291e-01 4.823e-01 1.926 0.054046 .
LOC_121 7.799e-01 4.376e-01 1.782 0.074714 .
LOC_131 8.112e-01 4.283e-01 1.894 0.058237 .
LOC_151 2.002e-02 4.061e-01 0.049 0.960677
LOC_161 1.320e-01 4.590e-01 0.288 0.773621
LOC_171 2.568e-01 4.188e-01 0.613 0.539833
LOC_181 -1.860e-02 4.458e-01 -0.042 0.966724
LOC_191 -6.280e-03 4.950e-01 -0.013 0.989877
LOC_201 7.418e-01 4.509e-01 1.645 0.099921 .
LOC_211 1.609e-01 4.250e-01 0.379 0.704920
LOC_221 -4.295e-02 4.216e-01 -0.102 0.918847
LOC_231 4.034e-02 5.058e-01 0.080 0.936434
LOC_241 7.717e-01 4.567e-01 1.690 0.091071 .
LOC_251 3.592e-01 4.198e-01 0.856 0.392177
LOC_261 1.758e-01 4.230e-01 0.416 0.677683
LOC_271 5.371e-01 4.438e-01 1.210 0.226161
LOC_281 1.199e-01 4.420e-01 0.271 0.786223
LOC_291 2.495e-01 4.370e-01 0.571 0.568078
LOC_301 7.391e-01 4.407e-01 1.677 0.093535 .
LOC_311 -7.783e-02 4.964e-01 -0.157 0.875415
LOC_321 2.973e-01 4.459e-01 0.667 0.504872
LOC_331 -3.139e-01 6.489e-01 -0.484 0.628520
LOC_341 1.808e-02 4.196e-01 0.043 0.965627
LOC_351 7.770e-01 4.077e-01 1.906 0.056668 .
LOC_361 6.124e-01 4.391e-01 1.395 0.163089
LOC_371 5.719e-01 4.227e-01 1.353 0.176017
LOC_381 3.072e-01 5.212e-01 0.589 0.555670
LOC_391 3.126e-01 4.193e-01 0.745 0.455992
LOC_401 4.926e-01 4.138e-01 1.190 0.233891
LOC_411 5.062e-01 4.304e-01 1.176 0.239583
LOC_421 2.969e-01 4.349e-01 0.683 0.494841
LOC_441 -3.727e-01 6.658e-01 -0.560 0.575668
LOC_451 2.172e-01 4.307e-01 0.504 0.613998
LOC_461 5.434e-01 4.462e-01 1.218 0.223253
LOC_471 8.097e-02 4.356e-01 0.186 0.852531
LOC_481 1.343e+00 4.775e-01 2.812 0.004928 **
LOC_491 2.728e-01 4.337e-01 0.629 0.529372
LOC_501 -5.084e-01 7.120e-01 -0.714 0.475178
LOC_511 6.877e-01 4.710e-01 1.460 0.144243
LOC_531 9.903e-01 4.324e-01 2.290 0.022008 *
LOC_541 -9.079e-02 4.551e-01 -0.200 0.841862
LOC_551 -2.414e-01 4.466e-01 -0.541 0.588839
TOA_101 -1.139e+00 2.079e-01 -5.478 4.31e-08 ***
TOA_151 -1.068e+00 2.129e-01 -5.017 5.26e-07 ***
TOA_201 2.039e-02 2.798e-01 0.073 0.941895
TOA_301 -6.994e-01 2.234e-01 -3.130 0.001746 **
TOA_321 -3.206e-01 8.046e-01 -0.399 0.690249
TOA_351 -1.490e+00 3.609e-01 -4.128 3.65e-05 ***
TOA_381 -7.377e-01 2.134e-01 -3.457 0.000545 ***
TOA_401 -1.035e+00 2.705e-01 -3.827 0.000130 ***
TOA_421 7.756e-02 4.894e-01 0.158 0.874063
TOA_441 1.323e+00 1.131e+00 1.170 0.242085
TOA_451 8.126e+00 1.393e+02 0.058 0.953469
PPGROUP_111 3.712e-01 1.578e-01 2.353 0.018644 *
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
(Dispersion parameter for binomial family taken to be 1)
Null deviance: 20683 on 14919 degrees of freedom
Residual deviance: 13457 on 14831 degrees of freedom
AIC: 13635
Number of Fisher Scoring iterations: 10
VIF
GSEGRD 14.102411
IndAvgSalary 42.867169
SalaryOverUnderIndAvg 9.596934
LowerLimitAge 219.397663
BLS_FEDERAL_OtherSep_Rate 30.490313
BLS_FEDERAL_Quits_Rate 698.268418
BLS_FEDERAL_TotalSep_Level 4264.767790
BLS_FEDERAL_OtherSep_Level 204.909502
BLS_FEDERAL_Quits_Level 1081.666363
BLS_FEDERAL_JobOpenings_Level 11.391217
BLS_FEDERAL_Layoffs_Rate 1354.785981
BLS_FEDERAL_Layoffs_Level 1017.591476
BLS_FEDERAL_TotalSep_Rate 1117.131916
SALARYLog 102.663330
LOSSqrt 1.974899
SEPCount_EFDATE_OCCLog 1.123968
SEPCount_EFDATE_LOCLog 24.878470
IndAvgSalaryLog 141.984104
AGELVL_B 27.781513
AGELVL_C 207.533042
AGELVL_D 237.591178
AGELVL_E 149.593271
AGELVL_F 83.959156
AGELVL_G 45.563783
AGELVL_H 23.767317
AGELVL_I 9.363424
LOC_01 6.064846
LOC_02 2.930911
LOC_04 9.618857
LOC_05 3.221621
LOC_06 37.559058
LOC_08 11.704767
LOC_09 2.216146
LOC_10 1.396248
LOC_11 53.613146
LOC_12 14.284626
LOC_13 13.978394
LOC_15 6.251134
LOC_16 2.646779
LOC_17 8.166015
LOC_18 3.090363
LOC_19 2.150571
LOC_20 2.853493
LOC_21 4.184083
LOC_22 3.997274
LOC_23 2.108420
LOC_24 35.764621
LOC_25 5.010330
LOC_26 4.475522
LOC_27 3.169450
LOC_28 3.120200
LOC_29 6.125775
LOC_30 3.287498
LOC_31 2.114779
LOC_32 2.926428
LOC_33 1.516780
LOC_34 4.418664
LOC_35 7.448987
LOC_36 9.534560
LOC_37 7.737850
LOC_38 1.953249
LOC_39 8.819970
LOC_40 6.133611
LOC_41 4.741161
LOC_42 9.214579
LOC_44 1.485738
LOC_45 3.639638
LOC_46 2.899271
LOC_47 4.023972
LOC_48 29.418807
LOC_49 4.212852
LOC_50 1.410219
LOC_51 41.569863
LOC_53 11.610658
LOC_54 2.711236
LOC_55 2.998441
TOA_10 22.487254
TOA_15 12.132765
TOA_20 2.122214
TOA_30 7.366183
TOA_32 1.076719
TOA_35 1.552401
TOA_38 10.935927
TOA_40 2.363610
TOA_42 1.216749
TOA_44 1.039597
TOA_45 1.000003
PPGROUP_11 1.894311
Removed BEFORE this step: BLS_FEDERAL_TotalSep_Level
Call:
glm(formula = fla, family = "binomial", data = OPMAnalysisDataNoFamBinary)
Deviance Residuals:
Min 1Q Median 3Q Max
-3.0328 -0.6692 -0.1043 0.7330 3.3237
Coefficients:
Estimate Std. Error z value Pr(>|z|)
(Intercept) -3.825e+01 6.262e+00 -6.108 1.01e-09 ***
GSEGRD -5.803e-01 4.732e-02 -12.262 < 2e-16 ***
IndAvgSalary -1.264e-05 4.668e-06 -2.708 0.006768 **
SalaryOverUnderIndAvg -2.024e-06 6.635e-06 -0.305 0.760301
LowerLimitAge -5.575e-02 2.693e-02 -2.070 0.038420 *
BLS_FEDERAL_OtherSep_Rate 3.611e+01 1.437e+00 25.120 < 2e-16 ***
BLS_FEDERAL_Quits_Rate 7.270e+01 5.219e+00 13.930 < 2e-16 ***
BLS_FEDERAL_OtherSep_Level -1.197e+00 6.082e-02 -19.678 < 2e-16 ***
BLS_FEDERAL_Quits_Level -1.509e+00 1.523e-01 -9.908 < 2e-16 ***
BLS_FEDERAL_JobOpenings_Level -1.101e-02 4.884e-03 -2.254 0.024227 *
BLS_FEDERAL_Layoffs_Rate -4.466e+01 2.329e+00 -19.178 < 2e-16 ***
BLS_FEDERAL_Layoffs_Level 2.495e+00 1.203e-01 20.749 < 2e-16 ***
BLS_FEDERAL_TotalSep_Rate -1.846e+01 9.166e-01 -20.133 < 2e-16 ***
SALARYLog 3.945e-01 7.037e-01 0.561 0.575088
LOSSqrt -7.747e-01 2.429e-02 -31.889 < 2e-16 ***
SEPCount_EFDATE_OCCLog -5.918e-02 1.696e-02 -3.489 0.000484 ***
SEPCount_EFDATE_LOCLog -3.755e-01 1.238e-01 -3.032 0.002429 **
IndAvgSalaryLog 4.163e+00 8.553e-01 4.867 1.13e-06 ***
AGELVL_B1 -9.569e-01 1.153e+00 -0.830 0.406660
AGELVL_C1 -3.480e-01 9.991e-01 -0.348 0.727615
AGELVL_D1 -1.336e-01 8.639e-01 -0.155 0.877130
AGELVL_E1 1.852e-01 7.302e-01 0.254 0.799760
AGELVL_F1 4.385e-01 5.973e-01 0.734 0.462897
AGELVL_G1 7.301e-01 4.650e-01 1.570 0.116409
AGELVL_H1 8.831e-01 3.341e-01 2.643 0.008217 **
AGELVL_I1 7.539e-01 2.079e-01 3.626 0.000288 ***
LOC_011 -2.489e-02 4.133e-01 -0.060 0.951969
LOC_021 1.510e-01 4.498e-01 0.336 0.737064
LOC_041 8.026e-01 4.170e-01 1.925 0.054264 .
LOC_051 1.123e-01 4.351e-01 0.258 0.796271
LOC_061 1.251e+00 4.878e-01 2.565 0.010307 *
LOC_081 5.257e-01 4.209e-01 1.249 0.211678
LOC_091 -4.061e-01 4.990e-01 -0.814 0.415776
LOC_101 -5.356e-01 7.189e-01 -0.745 0.456298
LOC_111 6.776e-01 4.728e-01 1.433 0.151837
LOC_121 6.051e-01 4.327e-01 1.399 0.161963
LOC_131 6.405e-01 4.235e-01 1.512 0.130476
LOC_151 -4.301e-02 4.056e-01 -0.106 0.915541
LOC_161 1.038e-01 4.589e-01 0.226 0.820988
LOC_171 1.353e-01 4.165e-01 0.325 0.745356
LOC_181 -7.591e-02 4.456e-01 -0.170 0.864729
LOC_191 2.355e-02 4.949e-01 0.048 0.962042
LOC_201 7.145e-01 4.509e-01 1.585 0.113007
LOC_211 9.266e-02 4.243e-01 0.218 0.827113
LOC_221 -9.093e-02 4.213e-01 -0.216 0.829126
LOC_231 8.372e-02 5.052e-01 0.166 0.868380
LOC_241 5.540e-01 4.493e-01 1.233 0.217637
LOC_251 2.784e-01 4.189e-01 0.665 0.506310
LOC_261 9.719e-02 4.222e-01 0.230 0.817932
LOC_271 4.773e-01 4.436e-01 1.076 0.281896
LOC_281 8.483e-02 4.417e-01 0.192 0.847713
LOC_291 1.100e-01 4.339e-01 0.253 0.799936
LOC_301 6.770e-01 4.402e-01 1.538 0.124030
LOC_311 -7.151e-02 4.963e-01 -0.144 0.885438
LOC_321 2.643e-01 4.459e-01 0.593 0.553388
LOC_331 -2.274e-01 6.493e-01 -0.350 0.726196
LOC_341 -4.064e-02 4.191e-01 -0.097 0.922736
LOC_351 6.872e-01 4.065e-01 1.691 0.090881 .
LOC_361 4.416e-01 4.344e-01 1.017 0.309360
LOC_371 4.401e-01 4.199e-01 1.048 0.294601
LOC_381 3.386e-01 5.221e-01 0.648 0.516685
LOC_391 1.852e-01 4.167e-01 0.444 0.656733
LOC_401 4.046e-01 4.127e-01 0.980 0.326849
LOC_411 4.058e-01 4.288e-01 0.946 0.344025
LOC_421 1.391e-01 4.309e-01 0.323 0.746886
LOC_441 -2.710e-01 6.641e-01 -0.408 0.683182
LOC_451 1.635e-01 4.303e-01 0.380 0.703905
LOC_461 5.476e-01 4.460e-01 1.228 0.219567
LOC_471 -8.055e-03 4.345e-01 -0.019 0.985210
LOC_481 1.100e+00 4.686e-01 2.347 0.018911 *
LOC_491 1.832e-01 4.327e-01 0.423 0.672075
LOC_501 -4.124e-01 7.077e-01 -0.583 0.560085
LOC_511 4.495e-01 4.623e-01 0.972 0.330963
LOC_531 8.255e-01 4.280e-01 1.929 0.053792 .
LOC_541 -1.036e-01 4.548e-01 -0.228 0.819877
LOC_551 -2.887e-01 4.462e-01 -0.647 0.517593
TOA_101 -1.140e+00 2.078e-01 -5.483 4.18e-08 ***
TOA_151 -1.069e+00 2.128e-01 -5.022 5.11e-07 ***
TOA_201 1.937e-02 2.797e-01 0.069 0.944790
TOA_301 -6.997e-01 2.234e-01 -3.131 0.001740 **
TOA_321 -3.406e-01 8.046e-01 -0.423 0.672023
TOA_351 -1.487e+00 3.610e-01 -4.120 3.79e-05 ***
TOA_381 -7.373e-01 2.133e-01 -3.456 0.000548 ***
TOA_401 -1.028e+00 2.703e-01 -3.802 0.000143 ***
TOA_421 7.901e-02 4.895e-01 0.161 0.871768
TOA_441 1.331e+00 1.131e+00 1.177 0.239227
TOA_451 8.162e+00 1.391e+02 0.059 0.953197
PPGROUP_111 3.640e-01 1.578e-01 2.306 0.021084 *
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
(Dispersion parameter for binomial family taken to be 1)
Null deviance: 20683 on 14919 degrees of freedom
Residual deviance: 13464 on 14832 degrees of freedom
AIC: 13640
Number of Fisher Scoring iterations: 10
VIF
GSEGRD 14.096450
IndAvgSalary 42.844549
SalaryOverUnderIndAvg 9.594943
LowerLimitAge 219.535659
BLS_FEDERAL_OtherSep_Rate 29.584276
BLS_FEDERAL_Quits_Rate 300.982891
BLS_FEDERAL_OtherSep_Level 43.194480
BLS_FEDERAL_Quits_Level 223.370966
BLS_FEDERAL_JobOpenings_Level 6.852760
BLS_FEDERAL_Layoffs_Rate 479.157701
BLS_FEDERAL_Layoffs_Level 961.396403
BLS_FEDERAL_TotalSep_Rate 161.354837
SALARYLog 102.645636
LOSSqrt 1.974931
SEPCount_EFDATE_OCCLog 1.123858
SEPCount_EFDATE_LOCLog 22.542581
IndAvgSalaryLog 141.961235
AGELVL_B 27.854483
AGELVL_C 207.826584
AGELVL_D 237.746047
AGELVL_E 149.661959
AGELVL_F 84.060896
AGELVL_G 45.615524
AGELVL_H 23.780677
AGELVL_I 9.360065
LOC_01 6.017956
LOC_02 2.935738
LOC_04 9.525681
LOC_05 3.231022
LOC_06 36.005770
LOC_08 11.534754
LOC_09 2.214122
LOC_10 1.395777
LOC_11 51.513697
LOC_12 13.994128
LOC_13 13.681734
LOC_15 6.251670
LOC_16 2.648036
LOC_17 8.078119
LOC_18 3.082752
LOC_19 2.152426
LOC_20 2.857458
LOC_21 4.178398
LOC_22 3.997988
LOC_23 2.112516
LOC_24 34.606682
LOC_25 4.988039
LOC_26 4.458787
LOC_27 3.158467
LOC_28 3.128964
LOC_29 6.056461
LOC_30 3.277764
LOC_31 2.118724
LOC_32 2.925017
LOC_33 1.510859
LOC_34 4.423038
LOC_35 7.410529
LOC_36 9.336726
LOC_37 7.638325
LOC_38 1.947152
LOC_39 8.704343
LOC_40 6.097745
LOC_41 4.710050
LOC_42 9.070910
LOC_44 1.482675
LOC_45 3.640281
LOC_46 2.910038
LOC_47 4.004226
LOC_48 28.340768
LOC_49 4.180681
LOC_50 1.413411
LOC_51 40.067925
LOC_53 11.382561
LOC_54 2.722130
LOC_55 3.001285
TOA_10 22.494780
TOA_15 12.133793
TOA_20 2.122048
TOA_30 7.365433
TOA_32 1.076605
TOA_35 1.551310
TOA_38 10.941032
TOA_40 2.367088
TOA_42 1.216410
TOA_44 1.039596
TOA_45 1.000003
PPGROUP_11 1.893480
Removed BEFORE this step: BLS_FEDERAL_Layoffs_Level
Call:
glm(formula = fla, family = "binomial", data = OPMAnalysisDataNoFamBinary)
Deviance Residuals:
Min 1Q Median 3Q Max
-3.0115 -0.7000 -0.1278 0.7851 3.3366
Coefficients:
Estimate Std. Error z value Pr(>|z|)
(Intercept) -4.301e+01 6.156e+00 -6.986 2.83e-12 ***
GSEGRD -5.595e-01 4.647e-02 -12.041 < 2e-16 ***
IndAvgSalary -1.328e-05 4.595e-06 -2.890 0.003849 **
SalaryOverUnderIndAvg -1.306e-06 6.577e-06 -0.199 0.842590
LowerLimitAge -5.586e-02 2.664e-02 -2.097 0.036012 *
BLS_FEDERAL_OtherSep_Rate 1.441e+01 9.446e-01 15.251 < 2e-16 ***
BLS_FEDERAL_Quits_Rate -2.888e+01 1.885e+00 -15.319 < 2e-16 ***
BLS_FEDERAL_OtherSep_Level -3.046e-01 4.075e-02 -7.475 7.74e-14 ***
BLS_FEDERAL_Quits_Level 1.250e+00 7.716e-02 16.202 < 2e-16 ***
BLS_FEDERAL_JobOpenings_Level 6.307e-02 3.425e-03 18.417 < 2e-16 ***
BLS_FEDERAL_Layoffs_Rate 2.640e+00 4.573e-01 5.773 7.79e-09 ***
BLS_FEDERAL_TotalSep_Rate -2.309e+00 4.736e-01 -4.876 1.08e-06 ***
SALARYLog 2.788e-01 6.943e-01 0.402 0.688043
LOSSqrt -7.634e-01 2.390e-02 -31.935 < 2e-16 ***
SEPCount_EFDATE_OCCLog -4.145e-02 1.658e-02 -2.500 0.012417 *
SEPCount_EFDATE_LOCLog -3.382e-01 1.209e-01 -2.798 0.005147 **
IndAvgSalaryLog 4.197e+00 8.427e-01 4.981 6.33e-07 ***
AGELVL_B1 -9.617e-01 1.140e+00 -0.843 0.398987
AGELVL_C1 -3.829e-01 9.882e-01 -0.388 0.698373
AGELVL_D1 -2.021e-01 8.545e-01 -0.237 0.813033
AGELVL_E1 1.062e-01 7.222e-01 0.147 0.883119
AGELVL_F1 3.640e-01 5.906e-01 0.616 0.537678
AGELVL_G1 6.340e-01 4.597e-01 1.379 0.167900
AGELVL_H1 8.107e-01 3.302e-01 2.455 0.014080 *
AGELVL_I1 6.997e-01 2.054e-01 3.407 0.000657 ***
LOC_011 -1.114e-01 4.073e-01 -0.273 0.784470
LOC_021 1.251e-01 4.408e-01 0.284 0.776495
LOC_041 6.939e-01 4.106e-01 1.690 0.091053 .
LOC_051 -3.647e-02 4.272e-01 -0.085 0.931970
LOC_061 1.061e+00 4.794e-01 2.213 0.026896 *
LOC_081 4.094e-01 4.141e-01 0.989 0.322851
LOC_091 -4.186e-01 4.854e-01 -0.862 0.388531
LOC_101 -5.427e-01 7.058e-01 -0.769 0.441931
LOC_111 5.141e-01 4.647e-01 1.106 0.268631
LOC_121 4.190e-01 4.257e-01 0.984 0.324987
LOC_131 4.778e-01 4.166e-01 1.147 0.251420
LOC_151 -9.066e-02 3.992e-01 -0.227 0.820361
LOC_161 -3.610e-02 4.510e-01 -0.080 0.936203
LOC_171 -4.924e-02 4.098e-01 -0.120 0.904368
LOC_181 -2.057e-01 4.380e-01 -0.470 0.638567
LOC_191 -1.762e-02 4.829e-01 -0.036 0.970893
LOC_201 5.917e-01 4.412e-01 1.341 0.179854
LOC_211 8.412e-03 4.172e-01 0.020 0.983914
LOC_221 -1.913e-01 4.142e-01 -0.462 0.644123
LOC_231 -3.702e-02 4.980e-01 -0.074 0.940752
LOC_241 3.930e-01 4.416e-01 0.890 0.373525
LOC_251 2.137e-01 4.108e-01 0.520 0.602867
LOC_261 7.013e-03 4.166e-01 0.017 0.986570
LOC_271 3.258e-01 4.344e-01 0.750 0.453240
LOC_281 -1.006e-01 4.353e-01 -0.231 0.817261
LOC_291 -7.823e-02 4.271e-01 -0.183 0.854680
LOC_301 5.859e-01 4.354e-01 1.346 0.178428
LOC_311 -4.788e-02 4.887e-01 -0.098 0.921945
LOC_321 1.512e-01 4.369e-01 0.346 0.729279
LOC_331 -1.695e-01 6.453e-01 -0.263 0.792798
LOC_341 -1.284e-01 4.115e-01 -0.312 0.755048
LOC_351 5.401e-01 4.005e-01 1.349 0.177458
LOC_361 3.195e-01 4.272e-01 0.748 0.454550
LOC_371 3.255e-01 4.135e-01 0.787 0.431070
LOC_381 2.216e-01 5.142e-01 0.431 0.666465
LOC_391 -9.349e-03 4.103e-01 -0.023 0.981821
LOC_401 3.098e-01 4.056e-01 0.764 0.445001
LOC_411 2.935e-01 4.229e-01 0.694 0.487631
LOC_421 1.251e-02 4.242e-01 0.029 0.976477
LOC_441 -2.957e-01 6.472e-01 -0.457 0.647706
LOC_451 2.051e-02 4.243e-01 0.048 0.961442
LOC_461 5.812e-01 4.397e-01 1.322 0.186200
LOC_471 -1.252e-01 4.282e-01 -0.292 0.770029
LOC_481 8.834e-01 4.604e-01 1.919 0.055020 .
LOC_491 3.150e-02 4.257e-01 0.074 0.941023
LOC_501 -6.199e-01 6.984e-01 -0.888 0.374790
LOC_511 3.074e-01 4.546e-01 0.676 0.498843
LOC_531 7.209e-01 4.215e-01 1.710 0.087190 .
LOC_541 -2.475e-01 4.481e-01 -0.552 0.580680
LOC_551 -3.552e-01 4.389e-01 -0.809 0.418429
TOA_101 -1.122e+00 2.031e-01 -5.527 3.25e-08 ***
TOA_151 -1.070e+00 2.082e-01 -5.141 2.73e-07 ***
TOA_201 4.745e-02 2.746e-01 0.173 0.862841
TOA_301 -7.185e-01 2.184e-01 -3.290 0.001003 **
TOA_321 -4.425e-01 8.108e-01 -0.546 0.585256
TOA_351 -1.555e+00 3.537e-01 -4.395 1.11e-05 ***
TOA_381 -7.398e-01 2.086e-01 -3.546 0.000390 ***
TOA_401 -1.025e+00 2.648e-01 -3.872 0.000108 ***
TOA_421 1.070e-01 4.846e-01 0.221 0.825281
TOA_441 9.625e-01 1.098e+00 0.876 0.380817
TOA_451 8.185e+00 1.344e+02 0.061 0.951423
PPGROUP_111 2.733e-01 1.548e-01 1.765 0.077573 .
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
(Dispersion parameter for binomial family taken to be 1)
Null deviance: 20683 on 14919 degrees of freedom
Residual deviance: 13915 on 14833 degrees of freedom
AIC: 14089
Number of Fisher Scoring iterations: 10
VIF
GSEGRD 14.084820
IndAvgSalary 42.946627
SalaryOverUnderIndAvg 9.659062
LowerLimitAge 222.204956
BLS_FEDERAL_OtherSep_Rate 13.419915
BLS_FEDERAL_Quits_Rate 38.717795
BLS_FEDERAL_OtherSep_Level 20.402730
BLS_FEDERAL_Quits_Level 55.691958
BLS_FEDERAL_JobOpenings_Level 3.613481
BLS_FEDERAL_Layoffs_Rate 18.823252
BLS_FEDERAL_TotalSep_Rate 44.241110
SALARYLog 103.354860
LOSSqrt 1.954522
SEPCount_EFDATE_OCCLog 1.120809
SEPCount_EFDATE_LOCLog 22.385361
IndAvgSalaryLog 142.796733
AGELVL_B 28.604589
AGELVL_C 211.730999
AGELVL_D 242.703461
AGELVL_E 153.427433
AGELVL_F 86.336131
AGELVL_G 46.508626
AGELVL_H 24.243731
AGELVL_I 9.382183
LOC_01 5.976635
LOC_02 3.003151
LOC_04 9.529767
LOC_05 3.271838
LOC_06 35.861969
LOC_08 11.622783
LOC_09 2.276671
LOC_10 1.398664
LOC_11 51.904733
LOC_12 14.130208
LOC_13 13.959043
LOC_15 6.339311
LOC_16 2.665564
LOC_17 8.090489
LOC_18 3.107284
LOC_19 2.194706
LOC_20 2.925662
LOC_21 4.229631
LOC_22 4.044529
LOC_23 2.101939
LOC_24 35.047823
LOC_25 5.170394
LOC_26 4.400034
LOC_27 3.237243
LOC_28 3.121063
LOC_29 6.334707
LOC_30 3.236488
LOC_31 2.118286
LOC_32 2.982528
LOC_33 1.493842
LOC_34 4.507625
LOC_35 7.329880
LOC_36 9.438289
LOC_37 7.597933
LOC_38 1.942086
LOC_39 8.669860
LOC_40 6.228079
LOC_41 4.673255
LOC_42 8.979397
LOC_44 1.495281
LOC_45 3.621144
LOC_46 2.900952
LOC_47 3.994933
LOC_48 28.968764
LOC_49 4.200175
LOC_50 1.410371
LOC_51 40.230985
LOC_53 11.359629
LOC_54 2.715109
LOC_55 3.016934
TOA_10 22.333791
TOA_15 12.080290
TOA_20 2.100723
TOA_30 7.351089
TOA_32 1.071734
TOA_35 1.551303
TOA_38 10.828279
TOA_40 2.348290
TOA_42 1.209936
TOA_44 1.039635
TOA_45 1.000003
PPGROUP_11 1.882840
Removed BEFORE this step: AGELVL_D
Call:
glm(formula = fla, family = "binomial", data = OPMAnalysisDataNoFamBinary)
Deviance Residuals:
Min 1Q Median 3Q Max
-3.0113 -0.7006 -0.1276 0.7849 3.3311
Coefficients:
Estimate Std. Error z value Pr(>|z|)
(Intercept) -4.340e+01 5.931e+00 -7.317 2.53e-13 ***
GSEGRD -5.595e-01 4.647e-02 -12.040 < 2e-16 ***
IndAvgSalary -1.329e-05 4.595e-06 -2.892 0.003832 **
SalaryOverUnderIndAvg -1.312e-06 6.577e-06 -0.200 0.841859
LowerLimitAge -4.960e-02 2.770e-03 -17.903 < 2e-16 ***
BLS_FEDERAL_OtherSep_Rate 1.440e+01 9.445e-01 15.250 < 2e-16 ***
BLS_FEDERAL_Quits_Rate -2.888e+01 1.885e+00 -15.322 < 2e-16 ***
BLS_FEDERAL_OtherSep_Level -3.045e-01 4.075e-02 -7.473 7.85e-14 ***
BLS_FEDERAL_Quits_Level 1.250e+00 7.716e-02 16.205 < 2e-16 ***
BLS_FEDERAL_JobOpenings_Level 6.307e-02 3.425e-03 18.416 < 2e-16 ***
BLS_FEDERAL_Layoffs_Rate 2.641e+00 4.573e-01 5.775 7.68e-09 ***
BLS_FEDERAL_TotalSep_Rate -2.310e+00 4.736e-01 -4.877 1.08e-06 ***
SALARYLog 2.798e-01 6.943e-01 0.403 0.686888
LOSSqrt -7.632e-01 2.389e-02 -31.943 < 2e-16 ***
SEPCount_EFDATE_OCCLog -4.141e-02 1.658e-02 -2.498 0.012490 *
SEPCount_EFDATE_LOCLog -3.388e-01 1.209e-01 -2.803 0.005070 **
IndAvgSalaryLog 4.196e+00 8.426e-01 4.980 6.35e-07 ***
AGELVL_B1 -6.976e-01 2.305e-01 -3.027 0.002471 **
AGELVL_C1 -1.502e-01 8.775e-02 -1.711 0.087052 .
AGELVL_E1 2.762e-01 6.920e-02 3.991 6.57e-05 ***
AGELVL_F1 5.027e-01 7.211e-02 6.971 3.14e-12 ***
AGELVL_G1 7.412e-01 7.599e-02 9.754 < 2e-16 ***
AGELVL_H1 8.866e-01 7.823e-02 11.333 < 2e-16 ***
AGELVL_I1 7.442e-01 8.273e-02 8.996 < 2e-16 ***
LOC_011 -1.115e-01 4.074e-01 -0.274 0.784377
LOC_021 1.249e-01 4.408e-01 0.283 0.776921
LOC_041 6.938e-01 4.107e-01 1.690 0.091121 .
LOC_051 -3.744e-02 4.272e-01 -0.088 0.930172
LOC_061 1.061e+00 4.794e-01 2.214 0.026852 *
LOC_081 4.100e-01 4.142e-01 0.990 0.322200
LOC_091 -4.181e-01 4.855e-01 -0.861 0.389181
LOC_101 -5.424e-01 7.058e-01 -0.768 0.442198
LOC_111 5.145e-01 4.648e-01 1.107 0.268330
LOC_121 4.192e-01 4.258e-01 0.985 0.324844
LOC_131 4.780e-01 4.167e-01 1.147 0.251381
LOC_151 -9.118e-02 3.993e-01 -0.228 0.819362
LOC_161 -3.764e-02 4.509e-01 -0.083 0.933484
LOC_171 -4.922e-02 4.099e-01 -0.120 0.904404
LOC_181 -2.057e-01 4.381e-01 -0.469 0.638725
LOC_191 -1.730e-02 4.830e-01 -0.036 0.971433
LOC_201 5.917e-01 4.412e-01 1.341 0.179920
LOC_211 8.037e-03 4.173e-01 0.019 0.984633
LOC_221 -1.919e-01 4.142e-01 -0.463 0.643121
LOC_231 -3.743e-02 4.982e-01 -0.075 0.940107
LOC_241 3.934e-01 4.417e-01 0.891 0.373092
LOC_251 2.131e-01 4.109e-01 0.519 0.604011
LOC_261 7.156e-03 4.167e-01 0.017 0.986298
LOC_271 3.251e-01 4.344e-01 0.748 0.454295
LOC_281 -1.013e-01 4.354e-01 -0.233 0.816057
LOC_291 -7.885e-02 4.272e-01 -0.185 0.853542
LOC_301 5.862e-01 4.355e-01 1.346 0.178275
LOC_311 -4.808e-02 4.888e-01 -0.098 0.921633
LOC_321 1.508e-01 4.370e-01 0.345 0.729965
LOC_331 -1.691e-01 6.454e-01 -0.262 0.793292
LOC_341 -1.288e-01 4.115e-01 -0.313 0.754263
LOC_351 5.406e-01 4.005e-01 1.350 0.177123
LOC_361 3.195e-01 4.273e-01 0.748 0.454542
LOC_371 3.257e-01 4.135e-01 0.788 0.430859
LOC_381 2.203e-01 5.142e-01 0.428 0.668306
LOC_391 -9.850e-03 4.103e-01 -0.024 0.980849
LOC_401 3.092e-01 4.057e-01 0.762 0.445988
LOC_411 2.936e-01 4.230e-01 0.694 0.487575
LOC_421 1.256e-02 4.242e-01 0.030 0.976378
LOC_441 -2.957e-01 6.472e-01 -0.457 0.647803
LOC_451 2.025e-02 4.244e-01 0.048 0.961946
LOC_461 5.802e-01 4.397e-01 1.320 0.186996
LOC_471 -1.261e-01 4.282e-01 -0.295 0.768343
LOC_481 8.839e-01 4.605e-01 1.920 0.054902 .
LOC_491 3.067e-02 4.258e-01 0.072 0.942572
LOC_501 -6.191e-01 6.988e-01 -0.886 0.375617
LOC_511 3.078e-01 4.546e-01 0.677 0.498454
LOC_531 7.210e-01 4.216e-01 1.710 0.087205 .
LOC_541 -2.478e-01 4.482e-01 -0.553 0.580332
LOC_551 -3.562e-01 4.390e-01 -0.811 0.417133
TOA_101 -1.122e+00 2.030e-01 -5.526 3.28e-08 ***
TOA_151 -1.070e+00 2.081e-01 -5.138 2.77e-07 ***
TOA_201 4.860e-02 2.746e-01 0.177 0.859519
TOA_301 -7.180e-01 2.184e-01 -3.288 0.001009 **
TOA_321 -4.426e-01 8.105e-01 -0.546 0.585051
TOA_351 -1.554e+00 3.537e-01 -4.393 1.12e-05 ***
TOA_381 -7.389e-01 2.085e-01 -3.543 0.000395 ***
TOA_401 -1.025e+00 2.648e-01 -3.869 0.000109 ***
TOA_421 1.081e-01 4.846e-01 0.223 0.823532
TOA_441 9.632e-01 1.098e+00 0.877 0.380492
TOA_451 8.186e+00 1.344e+02 0.061 0.951417
PPGROUP_111 2.739e-01 1.548e-01 1.769 0.076825 .
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
(Dispersion parameter for binomial family taken to be 1)
Null deviance: 20683 on 14919 degrees of freedom
Residual deviance: 13915 on 14834 degrees of freedom
AIC: 14087
Number of Fisher Scoring iterations: 10
VIF
GSEGRD 14.084657
IndAvgSalary 42.945520
SalaryOverUnderIndAvg 9.658435
LowerLimitAge 2.404092
BLS_FEDERAL_OtherSep_Rate 13.418547
BLS_FEDERAL_Quits_Rate 38.714968
BLS_FEDERAL_OtherSep_Level 20.402028
BLS_FEDERAL_Quits_Level 55.686488
BLS_FEDERAL_JobOpenings_Level 3.613370
BLS_FEDERAL_Layoffs_Rate 18.822912
BLS_FEDERAL_TotalSep_Rate 44.242917
SALARYLog 103.348057
LOSSqrt 1.952779
SEPCount_EFDATE_OCCLog 1.120721
SEPCount_EFDATE_LOCLog 22.379329
IndAvgSalaryLog 142.796845
AGELVL_B 1.168674
AGELVL_C 1.669532
AGELVL_E 1.408634
AGELVL_F 1.286868
AGELVL_G 1.270860
AGELVL_H 1.361018
AGELVL_I 1.522204
LOC_01 5.977821
LOC_02 3.003188
LOC_04 9.532952
LOC_05 3.272509
LOC_06 35.875014
LOC_08 11.623086
LOC_09 2.276701
LOC_10 1.398734
LOC_11 51.919874
LOC_12 14.132643
LOC_13 13.963587
LOC_15 6.341591
LOC_16 2.666040
LOC_17 8.092630
LOC_18 3.107775
LOC_19 2.194186
LOC_20 2.927057
LOC_21 4.230129
LOC_22 4.045685
LOC_23 2.101691
LOC_24 35.056214
LOC_25 5.172459
LOC_26 4.400721
LOC_27 3.238691
LOC_28 3.122100
LOC_29 6.337389
LOC_30 3.236332
LOC_31 2.118288
LOC_32 2.983542
LOC_33 1.493756
LOC_34 4.508410
LOC_35 7.331036
LOC_36 9.440269
LOC_37 7.599446
LOC_38 1.942321
LOC_39 8.672915
LOC_40 6.230374
LOC_41 4.674291
LOC_42 8.981797
LOC_44 1.495404
LOC_45 3.621864
LOC_46 2.901286
LOC_47 3.995110
LOC_48 28.978212
LOC_49 4.202065
LOC_50 1.410012
LOC_51 40.241437
LOC_53 11.362128
LOC_54 2.715366
LOC_55 3.017254
TOA_10 22.327307
TOA_15 12.074569
TOA_20 2.099988
TOA_30 7.349533
TOA_32 1.071774
TOA_35 1.551110
TOA_38 10.823001
TOA_40 2.347558
TOA_42 1.209745
TOA_44 1.039621
TOA_45 1.000003
PPGROUP_11 1.882529
Removed BEFORE this step: IndAvgSalaryLog
Call:
glm(formula = fla, family = "binomial", data = OPMAnalysisDataNoFamBinary)
Deviance Residuals:
Min 1Q Median 3Q Max
-3.0395 -0.7019 -0.1263 0.7840 3.3497
Coefficients:
Estimate Std. Error z value Pr(>|z|)
(Intercept) -2.644e+01 4.770e+00 -5.545 2.95e-08 ***
GSEGRD -4.496e-01 3.992e-02 -11.262 < 2e-16 ***
IndAvgSalary -1.934e-06 3.952e-06 -0.489 0.624588
SalaryOverUnderIndAvg -2.292e-05 4.847e-06 -4.729 2.26e-06 ***
LowerLimitAge -5.021e-02 2.763e-03 -18.173 < 2e-16 ***
BLS_FEDERAL_OtherSep_Rate 1.430e+01 9.432e-01 15.157 < 2e-16 ***
BLS_FEDERAL_Quits_Rate -2.875e+01 1.884e+00 -15.263 < 2e-16 ***
BLS_FEDERAL_OtherSep_Level -3.027e-01 4.071e-02 -7.434 1.05e-13 ***
BLS_FEDERAL_Quits_Level 1.245e+00 7.710e-02 16.154 < 2e-16 ***
BLS_FEDERAL_JobOpenings_Level 6.282e-02 3.420e-03 18.368 < 2e-16 ***
BLS_FEDERAL_Layoffs_Rate 2.624e+00 4.567e-01 5.746 9.16e-09 ***
BLS_FEDERAL_TotalSep_Rate -2.283e+00 4.730e-01 -4.826 1.39e-06 ***
SALARYLog 2.781e+00 4.702e-01 5.915 3.33e-09 ***
LOSSqrt -7.773e-01 2.378e-02 -32.691 < 2e-16 ***
SEPCount_EFDATE_OCCLog -4.269e-02 1.654e-02 -2.580 0.009867 **
SEPCount_EFDATE_LOCLog -3.384e-01 1.206e-01 -2.806 0.005020 **
AGELVL_B1 -7.060e-01 2.293e-01 -3.079 0.002077 **
AGELVL_C1 -1.436e-01 8.760e-02 -1.639 0.101226
AGELVL_E1 2.734e-01 6.908e-02 3.957 7.59e-05 ***
AGELVL_F1 4.953e-01 7.204e-02 6.877 6.13e-12 ***
AGELVL_G1 7.354e-01 7.592e-02 9.687 < 2e-16 ***
AGELVL_H1 8.824e-01 7.820e-02 11.284 < 2e-16 ***
AGELVL_I1 7.463e-01 8.273e-02 9.020 < 2e-16 ***
LOC_011 -1.231e-01 4.052e-01 -0.304 0.761344
LOC_021 3.745e-02 4.390e-01 0.085 0.932027
LOC_041 6.581e-01 4.085e-01 1.611 0.107178
LOC_051 -4.736e-02 4.248e-01 -0.111 0.911235
LOC_061 9.701e-01 4.771e-01 2.033 0.042035 *
LOC_081 3.861e-01 4.119e-01 0.937 0.348683
LOC_091 -5.468e-01 4.839e-01 -1.130 0.258549
LOC_101 -5.692e-01 6.976e-01 -0.816 0.414516
LOC_111 4.400e-01 4.626e-01 0.951 0.341499
LOC_121 3.965e-01 4.237e-01 0.936 0.349410
LOC_131 4.694e-01 4.145e-01 1.132 0.257439
LOC_151 -1.503e-01 3.971e-01 -0.379 0.705010
LOC_161 -6.249e-02 4.480e-01 -0.139 0.889065
LOC_171 -9.810e-02 4.077e-01 -0.241 0.809840
LOC_181 -2.354e-01 4.357e-01 -0.540 0.589030
LOC_191 -3.600e-02 4.806e-01 -0.075 0.940290
LOC_201 5.954e-01 4.383e-01 1.358 0.174363
LOC_211 3.674e-02 4.146e-01 0.089 0.929387
LOC_221 -2.066e-01 4.122e-01 -0.501 0.616202
LOC_231 -1.102e-01 4.970e-01 -0.222 0.824502
LOC_241 3.256e-01 4.395e-01 0.741 0.458834
LOC_251 1.379e-01 4.089e-01 0.337 0.736005
LOC_261 -5.016e-02 4.149e-01 -0.121 0.903763
LOC_271 2.662e-01 4.322e-01 0.616 0.537984
LOC_281 -1.075e-01 4.328e-01 -0.248 0.803895
LOC_291 -8.541e-02 4.248e-01 -0.201 0.840659
LOC_301 5.917e-01 4.322e-01 1.369 0.170951
LOC_311 -4.903e-02 4.859e-01 -0.101 0.919613
LOC_321 1.125e-01 4.350e-01 0.258 0.796022
LOC_331 -2.489e-01 6.473e-01 -0.385 0.700605
LOC_341 -2.017e-01 4.095e-01 -0.493 0.622246
LOC_351 5.241e-01 3.984e-01 1.316 0.188281
LOC_361 2.596e-01 4.252e-01 0.610 0.541536
LOC_371 3.067e-01 4.114e-01 0.746 0.455957
LOC_381 1.984e-01 5.107e-01 0.389 0.697620
LOC_391 -4.654e-02 4.083e-01 -0.114 0.909247
LOC_401 3.003e-01 4.034e-01 0.744 0.456696
LOC_411 2.491e-01 4.208e-01 0.592 0.553866
LOC_421 -2.432e-02 4.222e-01 -0.058 0.954061
LOC_441 -3.420e-01 6.440e-01 -0.531 0.595415
LOC_451 1.121e-02 4.221e-01 0.027 0.978806
LOC_461 5.917e-01 4.363e-01 1.356 0.175092
LOC_471 -1.381e-01 4.261e-01 -0.324 0.745863
LOC_481 8.442e-01 4.584e-01 1.842 0.065522 .
LOC_491 8.617e-03 4.239e-01 0.020 0.983780
LOC_501 -6.481e-01 6.984e-01 -0.928 0.353458
LOC_511 2.791e-01 4.526e-01 0.617 0.537366
LOC_531 6.714e-01 4.194e-01 1.601 0.109410
LOC_541 -2.588e-01 4.456e-01 -0.581 0.561371
LOC_551 -3.886e-01 4.369e-01 -0.889 0.373785
TOA_101 -1.102e+00 2.033e-01 -5.421 5.93e-08 ***
TOA_151 -1.027e+00 2.083e-01 -4.928 8.32e-07 ***
TOA_201 7.005e-02 2.747e-01 0.255 0.798677
TOA_301 -7.392e-01 2.187e-01 -3.379 0.000727 ***
TOA_321 -4.529e-01 8.135e-01 -0.557 0.577697
TOA_351 -1.611e+00 3.531e-01 -4.563 5.05e-06 ***
TOA_381 -7.100e-01 2.088e-01 -3.400 0.000673 ***
TOA_401 -1.065e+00 2.652e-01 -4.016 5.92e-05 ***
TOA_421 1.088e-01 4.845e-01 0.225 0.822336
TOA_441 9.136e-01 1.100e+00 0.831 0.406155
TOA_451 8.078e+00 1.343e+02 0.060 0.952049
PPGROUP_111 3.841e-01 1.524e-01 2.521 0.011706 *
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
(Dispersion parameter for binomial family taken to be 1)
Null deviance: 20683 on 14919 degrees of freedom
Residual deviance: 13940 on 14835 degrees of freedom
AIC: 14110
Number of Fisher Scoring iterations: 10
VIF
GSEGRD 10.387833
IndAvgSalary 31.307265
SalaryOverUnderIndAvg 5.136218
LowerLimitAge 2.394977
BLS_FEDERAL_OtherSep_Rate 13.403790
BLS_FEDERAL_Quits_Rate 38.696489
BLS_FEDERAL_OtherSep_Level 20.390819
BLS_FEDERAL_Quits_Level 55.669333
BLS_FEDERAL_JobOpenings_Level 3.612427
BLS_FEDERAL_Layoffs_Rate 18.792428
BLS_FEDERAL_TotalSep_Rate 44.170526
SALARYLog 47.439530
LOSSqrt 1.933243
SEPCount_EFDATE_OCCLog 1.119350
SEPCount_EFDATE_LOCLog 22.326922
AGELVL_B 1.170956
AGELVL_C 1.666558
AGELVL_E 1.408466
AGELVL_F 1.286235
AGELVL_G 1.271210
AGELVL_H 1.361442
AGELVL_I 1.523344
LOC_01 5.952585
LOC_02 2.962028
LOC_04 9.449940
LOC_05 3.261701
LOC_06 35.284461
LOC_08 11.610517
LOC_09 2.242291
LOC_10 1.404120
LOC_11 51.198890
LOC_12 14.028929
LOC_13 13.999906
LOC_15 6.267715
LOC_16 2.666982
LOC_17 8.026589
LOC_18 3.097305
LOC_19 2.186868
LOC_20 2.931389
LOC_21 4.233893
LOC_22 4.012930
LOC_23 2.079279
LOC_24 34.534464
LOC_25 5.089867
LOC_26 4.343076
LOC_27 3.214594
LOC_28 3.118743
LOC_29 6.347257
LOC_30 3.257016
LOC_31 2.115996
LOC_32 2.959568
LOC_33 1.479543
LOC_34 4.446378
LOC_35 7.270815
LOC_36 9.326787
LOC_37 7.551486
LOC_38 1.943499
LOC_39 8.604161
LOC_40 6.211647
LOC_41 4.641935
LOC_42 8.929351
LOC_44 1.492800
LOC_45 3.606664
LOC_46 2.914152
LOC_47 3.976128
LOC_48 28.823356
LOC_49 4.162500
LOC_50 1.403730
LOC_51 40.191955
LOC_53 11.266994
LOC_54 2.711282
LOC_55 2.996195
TOA_10 22.434899
TOA_15 12.162260
TOA_20 2.107040
TOA_30 7.345564
TOA_32 1.071489
TOA_35 1.556871
TOA_38 10.854026
TOA_40 2.345046
TOA_42 1.210866
TOA_44 1.039612
TOA_45 1.000003
PPGROUP_11 1.811161
Removed BEFORE this step: BLS_FEDERAL_Quits_Level
Call:
glm(formula = fla, family = "binomial", data = OPMAnalysisDataNoFamBinary)
Deviance Residuals:
Min 1Q Median 3Q Max
-2.9018 -0.7194 -0.1324 0.8061 3.3221
Coefficients:
Estimate Std. Error z value Pr(>|z|)
(Intercept) -2.234e+01 4.713e+00 -4.740 2.14e-06 ***
GSEGRD -4.359e-01 3.959e-02 -11.011 < 2e-16 ***
IndAvgSalary -3.036e-06 3.907e-06 -0.777 0.437199
SalaryOverUnderIndAvg -2.260e-05 4.810e-06 -4.699 2.61e-06 ***
LowerLimitAge -4.989e-02 2.733e-03 -18.259 < 2e-16 ***
BLS_FEDERAL_OtherSep_Rate 8.371e+00 8.470e-01 9.884 < 2e-16 ***
BLS_FEDERAL_Quits_Rate 4.560e-02 5.967e-01 0.076 0.939079
BLS_FEDERAL_OtherSep_Level -3.959e-01 3.951e-02 -10.020 < 2e-16 ***
BLS_FEDERAL_JobOpenings_Level 2.186e-02 2.260e-03 9.673 < 2e-16 ***
BLS_FEDERAL_Layoffs_Rate -2.568e+00 3.273e-01 -7.847 4.24e-15 ***
BLS_FEDERAL_TotalSep_Rate 3.397e+00 3.216e-01 10.564 < 2e-16 ***
SALARYLog 2.747e+00 4.657e-01 5.898 3.67e-09 ***
LOSSqrt -7.801e-01 2.357e-02 -33.097 < 2e-16 ***
SEPCount_EFDATE_OCCLog -3.452e-02 1.634e-02 -2.112 0.034675 *
SEPCount_EFDATE_LOCLog -4.259e-01 1.189e-01 -3.581 0.000342 ***
AGELVL_B1 -6.924e-01 2.268e-01 -3.053 0.002265 **
AGELVL_C1 -1.313e-01 8.679e-02 -1.513 0.130359
AGELVL_E1 2.763e-01 6.826e-02 4.047 5.18e-05 ***
AGELVL_F1 4.965e-01 7.115e-02 6.978 2.99e-12 ***
AGELVL_G1 7.324e-01 7.498e-02 9.768 < 2e-16 ***
AGELVL_H1 8.746e-01 7.728e-02 11.318 < 2e-16 ***
AGELVL_I1 7.430e-01 8.201e-02 9.060 < 2e-16 ***
LOC_011 -5.601e-02 3.999e-01 -0.140 0.888592
LOC_021 5.742e-02 4.337e-01 0.132 0.894672
LOC_041 7.592e-01 4.037e-01 1.881 0.060021 .
LOC_051 -5.827e-02 4.196e-01 -0.139 0.889549
LOC_061 1.181e+00 4.712e-01 2.507 0.012167 *
LOC_081 4.987e-01 4.071e-01 1.225 0.220606
LOC_091 -5.831e-01 4.824e-01 -1.209 0.226831
LOC_101 -5.733e-01 6.939e-01 -0.826 0.408690
LOC_111 6.625e-01 4.565e-01 1.451 0.146646
LOC_121 5.224e-01 4.184e-01 1.249 0.211797
LOC_131 5.565e-01 4.093e-01 1.360 0.173940
LOC_151 -1.345e-01 3.927e-01 -0.342 0.732000
LOC_161 -9.205e-02 4.434e-01 -0.208 0.835545
LOC_171 3.706e-02 4.025e-01 0.092 0.926642
LOC_181 -2.620e-01 4.319e-01 -0.607 0.544081
LOC_191 -8.922e-02 4.737e-01 -0.188 0.850606
LOC_201 5.594e-01 4.332e-01 1.291 0.196549
LOC_211 3.146e-02 4.106e-01 0.077 0.938932
LOC_221 -1.711e-01 4.074e-01 -0.420 0.674576
LOC_231 -2.614e-01 4.908e-01 -0.533 0.594349
LOC_241 4.839e-01 4.337e-01 1.116 0.264584
LOC_251 1.387e-01 4.046e-01 0.343 0.731702
LOC_261 -6.759e-02 4.111e-01 -0.164 0.869401
LOC_271 2.541e-01 4.276e-01 0.594 0.552435
LOC_281 -8.418e-02 4.287e-01 -0.196 0.844337
LOC_291 1.340e-02 4.193e-01 0.032 0.974500
LOC_301 6.609e-01 4.286e-01 1.542 0.123099
LOC_311 -5.048e-02 4.834e-01 -0.104 0.916821
LOC_321 7.871e-02 4.283e-01 0.184 0.854173
LOC_331 -3.510e-01 6.392e-01 -0.549 0.582960
LOC_341 -2.261e-01 4.054e-01 -0.558 0.577028
LOC_351 5.660e-01 3.939e-01 1.437 0.150762
LOC_361 3.725e-01 4.195e-01 0.888 0.374574
LOC_371 3.360e-01 4.063e-01 0.827 0.408266
LOC_381 1.128e-01 5.042e-01 0.224 0.822940
LOC_391 1.617e-02 4.036e-01 0.040 0.968049
LOC_401 3.390e-01 3.991e-01 0.850 0.395598
LOC_411 3.394e-01 4.159e-01 0.816 0.414509
LOC_421 1.121e-01 4.167e-01 0.269 0.787960
LOC_441 -5.137e-01 6.313e-01 -0.814 0.415793
LOC_451 2.444e-02 4.183e-01 0.058 0.953414
LOC_461 5.432e-01 4.329e-01 1.255 0.209539
LOC_471 -5.328e-02 4.208e-01 -0.127 0.899227
LOC_481 9.828e-01 4.522e-01 2.173 0.029768 *
LOC_491 2.927e-02 4.189e-01 0.070 0.944286
LOC_501 -9.052e-01 6.916e-01 -1.309 0.190582
LOC_511 4.560e-01 4.466e-01 1.021 0.307159
LOC_531 7.725e-01 4.143e-01 1.865 0.062218 .
LOC_541 -2.808e-01 4.407e-01 -0.637 0.524045
LOC_551 -3.654e-01 4.315e-01 -0.847 0.397159
TOA_101 -1.071e+00 1.998e-01 -5.360 8.31e-08 ***
TOA_151 -1.006e+00 2.047e-01 -4.911 9.04e-07 ***
TOA_201 1.431e-01 2.725e-01 0.525 0.599441
TOA_301 -7.371e-01 2.152e-01 -3.425 0.000614 ***
TOA_321 -6.133e-01 7.978e-01 -0.769 0.442008
TOA_351 -1.635e+00 3.487e-01 -4.690 2.73e-06 ***
TOA_381 -6.999e-01 2.053e-01 -3.409 0.000651 ***
TOA_401 -1.081e+00 2.610e-01 -4.141 3.47e-05 ***
TOA_421 1.781e-01 4.812e-01 0.370 0.711334
TOA_441 1.043e+00 1.093e+00 0.955 0.339649
TOA_451 8.391e+00 1.372e+02 0.061 0.951241
PPGROUP_111 2.885e-01 1.503e-01 1.919 0.054947 .
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
(Dispersion parameter for binomial family taken to be 1)
Null deviance: 20683 on 14919 degrees of freedom
Residual deviance: 14209 on 14836 degrees of freedom
AIC: 14377
Number of Fisher Scoring iterations: 10
VIF
GSEGRD 10.424568
IndAvgSalary 31.216174
SalaryOverUnderIndAvg 5.125810
LowerLimitAge 2.387892
BLS_FEDERAL_OtherSep_Rate 10.875194
BLS_FEDERAL_Quits_Rate 4.193359
BLS_FEDERAL_OtherSep_Level 19.417270
BLS_FEDERAL_JobOpenings_Level 1.583558
BLS_FEDERAL_Layoffs_Rate 9.650140
BLS_FEDERAL_TotalSep_Rate 20.424399
SALARYLog 47.466426
LOSSqrt 1.927259
SEPCount_EFDATE_OCCLog 1.117936
SEPCount_EFDATE_LOCLog 22.153474
AGELVL_B 1.171917
AGELVL_C 1.662913
AGELVL_E 1.408851
AGELVL_F 1.288061
AGELVL_G 1.274118
AGELVL_H 1.364700
AGELVL_I 1.523490
LOC_01 6.047920
LOC_02 2.980308
LOC_04 9.395239
LOC_05 3.281445
LOC_06 35.067245
LOC_08 11.530687
LOC_09 2.205900
LOC_10 1.401010
LOC_11 50.768507
LOC_12 13.990995
LOC_13 13.927554
LOC_15 6.250716
LOC_16 2.659772
LOC_17 8.087933
LOC_18 3.066479
LOC_19 2.206823
LOC_20 2.938253
LOC_21 4.188049
LOC_22 4.026488
LOC_23 2.091270
LOC_24 34.578086
LOC_25 5.046513
LOC_26 4.281943
LOC_27 3.203895
LOC_28 3.099333
LOC_29 6.320596
LOC_30 3.228871
LOC_31 2.091229
LOC_32 3.019173
LOC_33 1.482226
LOC_34 4.407396
LOC_35 7.197027
LOC_36 9.405915
LOC_37 7.613092
LOC_38 1.950080
LOC_39 8.532976
LOC_40 6.158756
LOC_41 4.640127
LOC_42 9.008812
LOC_44 1.506730
LOC_45 3.562722
LOC_46 2.885825
LOC_47 3.986924
LOC_48 28.862024
LOC_49 4.170763
LOC_50 1.402052
LOC_51 40.072217
LOC_53 11.336759
LOC_54 2.712683
LOC_55 3.013910
TOA_10 22.095151
TOA_15 11.934189
TOA_20 2.059844
TOA_30 7.207750
TOA_32 1.071480
TOA_35 1.548892
TOA_38 10.705362
TOA_40 2.331226
TOA_42 1.203877
TOA_44 1.038708
TOA_45 1.000003
PPGROUP_11 1.801222
Removed BEFORE this step: LOC_11
Call:
glm(formula = fla, family = "binomial", data = OPMAnalysisDataNoFamBinary)
Deviance Residuals:
Min 1Q Median 3Q Max
-2.9028 -0.7186 -0.1333 0.8045 3.3205
Coefficients:
Estimate Std. Error z value Pr(>|z|)
(Intercept) -2.261e+01 4.712e+00 -4.798 1.60e-06 ***
GSEGRD -4.352e-01 3.958e-02 -10.996 < 2e-16 ***
IndAvgSalary -3.102e-06 3.907e-06 -0.794 0.427148
SalaryOverUnderIndAvg -2.260e-05 4.810e-06 -4.698 2.62e-06 ***
LowerLimitAge -5.000e-02 2.732e-03 -18.303 < 2e-16 ***
BLS_FEDERAL_OtherSep_Rate 8.438e+00 8.457e-01 9.978 < 2e-16 ***
BLS_FEDERAL_Quits_Rate 4.259e-02 5.967e-01 0.071 0.943098
BLS_FEDERAL_OtherSep_Level -4.018e-01 3.931e-02 -10.221 < 2e-16 ***
BLS_FEDERAL_JobOpenings_Level 2.270e-02 2.185e-03 10.392 < 2e-16 ***
BLS_FEDERAL_Layoffs_Rate -2.560e+00 3.272e-01 -7.825 5.06e-15 ***
BLS_FEDERAL_TotalSep_Rate 3.381e+00 3.213e-01 10.523 < 2e-16 ***
SALARYLog 2.760e+00 4.656e-01 5.928 3.07e-09 ***
LOSSqrt -7.805e-01 2.358e-02 -33.108 < 2e-16 ***
SEPCount_EFDATE_OCCLog -3.382e-02 1.633e-02 -2.071 0.038382 *
SEPCount_EFDATE_LOCLog -3.192e-01 9.371e-02 -3.406 0.000658 ***
AGELVL_B1 -6.893e-01 2.268e-01 -3.039 0.002372 **
AGELVL_C1 -1.320e-01 8.679e-02 -1.521 0.128343
AGELVL_E1 2.757e-01 6.826e-02 4.039 5.37e-05 ***
AGELVL_F1 4.942e-01 7.112e-02 6.949 3.68e-12 ***
AGELVL_G1 7.318e-01 7.497e-02 9.761 < 2e-16 ***
AGELVL_H1 8.753e-01 7.727e-02 11.328 < 2e-16 ***
AGELVL_I1 7.427e-01 8.201e-02 9.057 < 2e-16 ***
LOC_011 -5.429e-01 2.186e-01 -2.484 0.012986 *
LOC_021 -3.766e-01 3.152e-01 -1.195 0.232105
LOC_041 2.316e-01 1.765e-01 1.312 0.189466
LOC_051 -4.678e-01 3.118e-01 -1.500 0.133599
LOC_061 5.149e-01 1.053e-01 4.891 1.00e-06 ***
LOC_081 -4.657e-02 1.577e-01 -0.295 0.767768
LOC_091 -9.159e-01 4.259e-01 -2.150 0.031536 *
LOC_101 -8.526e-01 6.668e-01 -1.279 0.200988
LOC_121 -4.949e-02 1.412e-01 -0.350 0.726003
LOC_131 8.769e-04 1.456e-01 0.006 0.995194
LOC_151 -6.004e-01 2.275e-01 -2.639 0.008317 **
LOC_161 -5.090e-01 3.385e-01 -1.504 0.132691
LOC_171 -4.812e-01 1.867e-01 -2.577 0.009977 **
LOC_181 -7.062e-01 3.059e-01 -2.309 0.020943 *
LOC_191 -4.538e-01 4.032e-01 -1.126 0.260362
LOC_201 1.330e-01 3.194e-01 0.417 0.677038
LOC_211 -4.292e-01 2.617e-01 -1.640 0.100952
LOC_221 -6.101e-01 2.742e-01 -2.225 0.026071 *
LOC_231 -6.030e-01 4.321e-01 -1.396 0.162826
LOC_241 -1.295e-01 9.790e-02 -1.323 0.185760
LOC_251 -3.378e-01 2.376e-01 -1.422 0.155029
LOC_261 -5.353e-01 2.563e-01 -2.089 0.036734 *
LOC_271 -1.917e-01 2.988e-01 -0.641 0.521268
LOC_281 -5.197e-01 3.074e-01 -1.691 0.090904 .
LOC_291 -5.223e-01 1.996e-01 -2.616 0.008895 **
LOC_301 2.088e-01 2.956e-01 0.706 0.479972
LOC_311 -4.305e-01 4.074e-01 -1.057 0.290665
LOC_321 -3.357e-01 3.206e-01 -1.047 0.295054
LOC_331 -6.502e-01 6.070e-01 -1.071 0.284063
LOC_341 -6.764e-01 2.621e-01 -2.581 0.009858 **
LOC_351 7.981e-02 2.084e-01 0.383 0.701730
LOC_361 -1.877e-01 1.652e-01 -1.136 0.255979
LOC_371 -1.861e-01 1.899e-01 -0.980 0.327027
LOC_381 -2.361e-01 4.447e-01 -0.531 0.595422
LOC_391 -5.073e-01 1.821e-01 -2.786 0.005332 **
LOC_401 -1.477e-01 2.174e-01 -0.679 0.496845
LOC_411 -1.571e-01 2.374e-01 -0.662 0.508030
LOC_421 -4.407e-01 1.697e-01 -2.597 0.009401 **
LOC_441 -8.120e-01 5.983e-01 -1.357 0.174682
LOC_451 -4.226e-01 2.842e-01 -1.487 0.136989
LOC_461 1.526e-01 3.404e-01 0.448 0.654044
LOC_471 -5.370e-01 2.577e-01 -2.084 0.037174 *
LOC_481 3.449e-01 1.066e-01 3.237 0.001210 **
LOC_491 -4.575e-01 2.521e-01 -1.815 0.069516 .
LOC_501 -1.188e+00 6.641e-01 -1.789 0.073561 .
LOC_511 -1.779e-01 9.329e-02 -1.907 0.056570 .
LOC_531 2.146e-01 1.553e-01 1.382 0.166843
LOC_541 -6.873e-01 3.415e-01 -2.013 0.044165 *
LOC_551 -7.985e-01 3.130e-01 -2.551 0.010744 *
TOA_101 -1.074e+00 1.998e-01 -5.374 7.68e-08 ***
TOA_151 -1.007e+00 2.047e-01 -4.918 8.73e-07 ***
TOA_201 1.419e-01 2.725e-01 0.521 0.602561
TOA_301 -7.353e-01 2.152e-01 -3.417 0.000633 ***
TOA_321 -6.111e-01 7.974e-01 -0.766 0.443466
TOA_351 -1.632e+00 3.487e-01 -4.680 2.87e-06 ***
TOA_381 -7.054e-01 2.053e-01 -3.436 0.000590 ***
TOA_401 -1.081e+00 2.610e-01 -4.141 3.45e-05 ***
TOA_421 1.774e-01 4.812e-01 0.369 0.712308
TOA_441 1.048e+00 1.093e+00 0.960 0.337239
TOA_451 8.400e+00 1.373e+02 0.061 0.951210
PPGROUP_111 2.932e-01 1.503e-01 1.951 0.051042 .
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
(Dispersion parameter for binomial family taken to be 1)
Null deviance: 20683 on 14919 degrees of freedom
Residual deviance: 14211 on 14837 degrees of freedom
AIC: 14377
Number of Fisher Scoring iterations: 10
VIF
GSEGRD 10.423477
IndAvgSalary 31.212140
SalaryOverUnderIndAvg 5.124258
LowerLimitAge 2.386331
BLS_FEDERAL_OtherSep_Rate 10.848400
BLS_FEDERAL_Quits_Rate 4.197164
BLS_FEDERAL_OtherSep_Level 19.227923
BLS_FEDERAL_JobOpenings_Level 1.480324
BLS_FEDERAL_Layoffs_Rate 9.649270
BLS_FEDERAL_TotalSep_Rate 20.404316
SALARYLog 47.446166
LOSSqrt 1.928047
SEPCount_EFDATE_OCCLog 1.116822
SEPCount_EFDATE_LOCLog 13.746535
AGELVL_B 1.171734
AGELVL_C 1.662693
AGELVL_E 1.408708
AGELVL_F 1.287400
AGELVL_G 1.274043
AGELVL_H 1.364665
AGELVL_I 1.523507
LOC_01 1.808712
LOC_02 1.573336
LOC_04 1.794369
LOC_05 1.813621
LOC_06 1.750672
LOC_08 1.731481
LOC_09 1.718502
LOC_10 1.298869
LOC_12 1.595397
LOC_13 1.763800
LOC_15 2.096169
LOC_16 1.553767
LOC_17 1.742101
LOC_18 1.538529
LOC_19 1.594760
LOC_20 1.597676
LOC_21 1.700998
LOC_22 1.824250
LOC_23 1.618931
LOC_24 1.761633
LOC_25 1.740938
LOC_26 1.665517
LOC_27 1.563316
LOC_28 1.592336
LOC_29 1.435470
LOC_30 1.533660
LOC_31 1.486334
LOC_32 1.689900
LOC_33 1.331779
LOC_34 1.844282
LOC_35 2.013207
LOC_36 1.458929
LOC_37 1.663214
LOC_38 1.514536
LOC_39 1.737623
LOC_40 1.828184
LOC_41 1.512959
LOC_42 1.495241
LOC_44 1.350909
LOC_45 1.645623
LOC_46 1.782745
LOC_47 1.497655
LOC_48 1.603522
LOC_49 1.508097
LOC_50 1.295152
LOC_51 1.749001
LOC_53 1.592749
LOC_54 1.628118
LOC_55 1.584264
TOA_10 22.096299
TOA_15 11.935872
TOA_20 2.059526
TOA_30 7.208034
TOA_32 1.071549
TOA_35 1.549060
TOA_38 10.701220
TOA_40 2.331845
TOA_42 1.203958
TOA_44 1.038700
TOA_45 1.000003
PPGROUP_11 1.799377
Removed BEFORE this step: SALARYLog
Call:
glm(formula = fla, family = "binomial", data = OPMAnalysisDataNoFamBinary)
Deviance Residuals:
Min 1Q Median 3Q Max
-2.9122 -0.7204 -0.1343 0.8061 3.2925
Coefficients:
Estimate Std. Error z value Pr(>|z|)
(Intercept) 4.987e+00 7.835e-01 6.364 1.96e-10 ***
GSEGRD -2.955e-01 3.080e-02 -9.594 < 2e-16 ***
IndAvgSalary 1.735e-05 1.898e-06 9.139 < 2e-16 ***
SalaryOverUnderIndAvg 2.150e-06 2.501e-06 0.860 0.389879
LowerLimitAge -4.920e-02 2.725e-03 -18.051 < 2e-16 ***
BLS_FEDERAL_OtherSep_Rate 8.403e+00 8.448e-01 9.948 < 2e-16 ***
BLS_FEDERAL_Quits_Rate 1.083e-01 5.959e-01 0.182 0.855824
BLS_FEDERAL_OtherSep_Level -4.021e-01 3.928e-02 -10.236 < 2e-16 ***
BLS_FEDERAL_JobOpenings_Level 2.275e-02 2.183e-03 10.423 < 2e-16 ***
BLS_FEDERAL_Layoffs_Rate -2.551e+00 3.267e-01 -7.808 5.79e-15 ***
BLS_FEDERAL_TotalSep_Rate 3.375e+00 3.209e-01 10.518 < 2e-16 ***
LOSSqrt -7.692e-01 2.344e-02 -32.816 < 2e-16 ***
SEPCount_EFDATE_OCCLog -3.335e-02 1.631e-02 -2.044 0.040935 *
SEPCount_EFDATE_LOCLog -3.133e-01 9.411e-02 -3.329 0.000872 ***
AGELVL_B1 -7.679e-01 2.251e-01 -3.411 0.000646 ***
AGELVL_C1 -1.606e-01 8.665e-02 -1.854 0.063762 .
AGELVL_E1 2.733e-01 6.827e-02 4.004 6.24e-05 ***
AGELVL_F1 4.870e-01 7.105e-02 6.855 7.14e-12 ***
AGELVL_G1 7.267e-01 7.486e-02 9.708 < 2e-16 ***
AGELVL_H1 8.658e-01 7.702e-02 11.240 < 2e-16 ***
AGELVL_I1 7.354e-01 8.171e-02 9.000 < 2e-16 ***
LOC_011 -5.557e-01 2.197e-01 -2.530 0.011416 *
LOC_021 -3.108e-01 3.146e-01 -0.988 0.323285
LOC_041 2.687e-01 1.765e-01 1.522 0.127996
LOC_051 -4.659e-01 3.134e-01 -1.487 0.137119
LOC_061 5.599e-01 1.046e-01 5.353 8.66e-08 ***
LOC_081 -3.527e-02 1.582e-01 -0.223 0.823590
LOC_091 -8.671e-01 4.267e-01 -2.032 0.042120 *
LOC_101 -8.265e-01 6.705e-01 -1.233 0.217680
LOC_121 -5.599e-02 1.417e-01 -0.395 0.692810
LOC_131 1.182e-02 1.458e-01 0.081 0.935377
LOC_151 -5.282e-01 2.274e-01 -2.323 0.020202 *
LOC_161 -5.101e-01 3.394e-01 -1.503 0.132880
LOC_171 -4.700e-01 1.873e-01 -2.509 0.012105 *
LOC_181 -7.294e-01 3.070e-01 -2.375 0.017530 *
LOC_191 -4.637e-01 4.050e-01 -1.145 0.252203
LOC_201 1.018e-01 3.209e-01 0.317 0.751116
LOC_211 -4.880e-01 2.630e-01 -1.855 0.063583 .
LOC_221 -6.270e-01 2.759e-01 -2.273 0.023048 *
LOC_231 -5.873e-01 4.336e-01 -1.354 0.175613
LOC_241 -1.037e-01 9.743e-02 -1.064 0.287105
LOC_251 -2.889e-01 2.379e-01 -1.214 0.224606
LOC_261 -5.339e-01 2.576e-01 -2.073 0.038204 *
LOC_271 -1.689e-01 2.997e-01 -0.563 0.573110
LOC_281 -5.542e-01 3.093e-01 -1.792 0.073106 .
LOC_291 -5.440e-01 2.005e-01 -2.713 0.006663 **
LOC_301 2.251e-01 2.959e-01 0.761 0.446735
LOC_311 -4.729e-01 4.088e-01 -1.157 0.247348
LOC_321 -3.299e-01 3.203e-01 -1.030 0.303020
LOC_331 -6.732e-01 6.067e-01 -1.110 0.267190
LOC_341 -6.022e-01 2.624e-01 -2.295 0.021717 *
LOC_351 8.758e-02 2.089e-01 0.419 0.675016
LOC_361 -1.700e-01 1.655e-01 -1.027 0.304265
LOC_371 -1.678e-01 1.903e-01 -0.882 0.377821
LOC_381 -2.658e-01 4.461e-01 -0.596 0.551256
LOC_391 -5.167e-01 1.826e-01 -2.830 0.004652 **
LOC_401 -1.585e-01 2.183e-01 -0.726 0.467640
LOC_411 -1.645e-01 2.382e-01 -0.691 0.489675
LOC_421 -4.383e-01 1.703e-01 -2.573 0.010082 *
LOC_441 -7.260e-01 5.975e-01 -1.215 0.224288
LOC_451 -4.328e-01 2.856e-01 -1.516 0.129621
LOC_461 1.962e-01 3.412e-01 0.575 0.565204
LOC_471 -5.533e-01 2.581e-01 -2.144 0.032070 *
LOC_481 3.532e-01 1.064e-01 3.318 0.000907 ***
LOC_491 -4.828e-01 2.539e-01 -1.901 0.057268 .
LOC_501 -1.227e+00 6.667e-01 -1.840 0.065731 .
LOC_511 -1.381e-01 9.239e-02 -1.494 0.135075
LOC_531 2.534e-01 1.554e-01 1.631 0.102945
LOC_541 -7.024e-01 3.430e-01 -2.048 0.040586 *
LOC_551 -8.292e-01 3.149e-01 -2.633 0.008458 **
TOA_101 -1.032e+00 2.005e-01 -5.146 2.66e-07 ***
TOA_151 -9.837e-01 2.057e-01 -4.783 1.73e-06 ***
TOA_201 1.385e-01 2.737e-01 0.506 0.612667
TOA_301 -7.417e-01 2.158e-01 -3.437 0.000588 ***
TOA_321 -6.169e-01 8.017e-01 -0.769 0.441625
TOA_351 -1.733e+00 3.489e-01 -4.968 6.76e-07 ***
TOA_381 -6.428e-01 2.059e-01 -3.121 0.001800 **
TOA_401 -1.089e+00 2.615e-01 -4.163 3.15e-05 ***
TOA_421 1.516e-01 4.816e-01 0.315 0.752882
TOA_441 9.702e-01 1.092e+00 0.889 0.374127
TOA_451 8.342e+00 1.373e+02 0.061 0.951546
PPGROUP_111 3.996e-01 1.470e-01 2.718 0.006574 **
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
(Dispersion parameter for binomial family taken to be 1)
Null deviance: 20683 on 14919 degrees of freedom
Residual deviance: 14247 on 14838 degrees of freedom
AIC: 14411
Number of Fisher Scoring iterations: 10
VIF
GSEGRD 6.354746
IndAvgSalary 7.387766
SalaryOverUnderIndAvg 1.390114
LowerLimitAge 2.385147
BLS_FEDERAL_OtherSep_Rate 10.845686
BLS_FEDERAL_Quits_Rate 4.193261
BLS_FEDERAL_OtherSep_Level 19.238975
BLS_FEDERAL_JobOpenings_Level 1.480661
BLS_FEDERAL_Layoffs_Rate 9.659385
BLS_FEDERAL_TotalSep_Rate 20.421006
LOSSqrt 1.918764
SEPCount_EFDATE_OCCLog 1.115914
SEPCount_EFDATE_LOCLog 13.892472
AGELVL_B 1.175315
AGELVL_C 1.657862
AGELVL_E 1.408735
AGELVL_F 1.286049
AGELVL_G 1.273136
AGELVL_H 1.363132
AGELVL_I 1.522601
LOC_01 1.807478
LOC_02 1.581945
LOC_04 1.801385
LOC_05 1.812283
LOC_06 1.737309
LOC_08 1.732556
LOC_09 1.726621
LOC_10 1.299121
LOC_12 1.591573
LOC_13 1.766830
LOC_15 2.108753
LOC_16 1.556842
LOC_17 1.743444
LOC_18 1.539210
LOC_19 1.596619
LOC_20 1.597037
LOC_21 1.696238
LOC_22 1.820706
LOC_23 1.621595
LOC_24 1.759641
LOC_25 1.744307
LOC_26 1.664777
LOC_27 1.566618
LOC_28 1.590589
LOC_29 1.434045
LOC_30 1.541124
LOC_31 1.488876
LOC_32 1.701623
LOC_33 1.337304
LOC_34 1.846826
LOC_35 2.022638
LOC_36 1.457910
LOC_37 1.665035
LOC_38 1.516009
LOC_39 1.738870
LOC_40 1.831806
LOC_41 1.513169
LOC_42 1.494536
LOC_44 1.355905
LOC_45 1.645585
LOC_46 1.789284
LOC_47 1.502473
LOC_48 1.596570
LOC_49 1.503876
LOC_50 1.296301
LOC_51 1.757573
LOC_53 1.591402
LOC_54 1.630070
LOC_55 1.581435
TOA_10 22.326646
TOA_15 12.110061
TOA_20 2.062102
TOA_30 7.350634
TOA_32 1.071336
TOA_35 1.551092
TOA_38 10.732947
TOA_40 2.346874
TOA_42 1.205710
TOA_44 1.038804
TOA_45 1.000003
PPGROUP_11 1.750610
Removed BEFORE this step: TOA_10
Call:
glm(formula = fla, family = "binomial", data = OPMAnalysisDataNoFamBinary)
Deviance Residuals:
Min 1Q Median 3Q Max
-2.9215 -0.7214 -0.1332 0.8072 3.3048
Coefficients:
Estimate Std. Error z value Pr(>|z|)
(Intercept) 4.041e+00 7.608e-01 5.312 1.09e-07 ***
GSEGRD -2.963e-01 3.065e-02 -9.667 < 2e-16 ***
IndAvgSalary 1.719e-05 1.893e-06 9.082 < 2e-16 ***
SalaryOverUnderIndAvg 2.365e-06 2.502e-06 0.945 0.344447
LowerLimitAge -4.878e-02 2.721e-03 -17.931 < 2e-16 ***
BLS_FEDERAL_OtherSep_Rate 8.451e+00 8.439e-01 10.014 < 2e-16 ***
BLS_FEDERAL_Quits_Rate 1.676e-01 5.943e-01 0.282 0.777969
BLS_FEDERAL_OtherSep_Level -4.039e-01 3.923e-02 -10.296 < 2e-16 ***
BLS_FEDERAL_JobOpenings_Level 2.261e-02 2.180e-03 10.368 < 2e-16 ***
BLS_FEDERAL_Layoffs_Rate -2.514e+00 3.261e-01 -7.708 1.28e-14 ***
BLS_FEDERAL_TotalSep_Rate 3.350e+00 3.204e-01 10.457 < 2e-16 ***
LOSSqrt -7.863e-01 2.325e-02 -33.822 < 2e-16 ***
SEPCount_EFDATE_OCCLog -3.478e-02 1.630e-02 -2.133 0.032932 *
SEPCount_EFDATE_LOCLog -3.075e-01 9.419e-02 -3.265 0.001095 **
AGELVL_B1 -7.477e-01 2.245e-01 -3.330 0.000867 ***
AGELVL_C1 -1.597e-01 8.638e-02 -1.848 0.064542 .
AGELVL_E1 2.741e-01 6.807e-02 4.027 5.65e-05 ***
AGELVL_F1 4.863e-01 7.094e-02 6.855 7.14e-12 ***
AGELVL_G1 7.226e-01 7.478e-02 9.663 < 2e-16 ***
AGELVL_H1 8.614e-01 7.707e-02 11.176 < 2e-16 ***
AGELVL_I1 7.289e-01 8.179e-02 8.911 < 2e-16 ***
LOC_011 -5.653e-01 2.202e-01 -2.567 0.010247 *
LOC_021 -3.169e-01 3.146e-01 -1.007 0.313817
LOC_041 2.542e-01 1.765e-01 1.440 0.149857
LOC_051 -4.773e-01 3.138e-01 -1.521 0.128211
LOC_061 5.547e-01 1.044e-01 5.313 1.08e-07 ***
LOC_081 -4.846e-02 1.582e-01 -0.306 0.759344
LOC_091 -8.718e-01 4.276e-01 -2.039 0.041488 *
LOC_101 -8.238e-01 6.711e-01 -1.228 0.219609
LOC_121 -5.109e-02 1.416e-01 -0.361 0.718248
LOC_131 9.091e-03 1.457e-01 0.062 0.950251
LOC_151 -5.436e-01 2.275e-01 -2.389 0.016876 *
LOC_161 -5.247e-01 3.399e-01 -1.544 0.122659
LOC_171 -4.739e-01 1.869e-01 -2.535 0.011230 *
LOC_181 -7.130e-01 3.068e-01 -2.324 0.020147 *
LOC_191 -4.297e-01 4.040e-01 -1.063 0.287556
LOC_201 9.455e-02 3.213e-01 0.294 0.768535
LOC_211 -5.051e-01 2.633e-01 -1.918 0.055063 .
LOC_221 -6.215e-01 2.763e-01 -2.249 0.024513 *
LOC_231 -6.024e-01 4.343e-01 -1.387 0.165415
LOC_241 -1.242e-01 9.734e-02 -1.276 0.202111
LOC_251 -2.691e-01 2.372e-01 -1.135 0.256559
LOC_261 -5.050e-01 2.566e-01 -1.968 0.049118 *
LOC_271 -1.794e-01 2.998e-01 -0.598 0.549698
LOC_281 -5.411e-01 3.080e-01 -1.757 0.078962 .
LOC_291 -5.541e-01 2.001e-01 -2.770 0.005612 **
LOC_301 2.159e-01 2.957e-01 0.730 0.465262
LOC_311 -4.906e-01 4.089e-01 -1.200 0.230181
LOC_321 -3.230e-01 3.195e-01 -1.011 0.311986
LOC_331 -6.771e-01 6.082e-01 -1.113 0.265596
LOC_341 -6.184e-01 2.626e-01 -2.355 0.018502 *
LOC_351 7.983e-02 2.089e-01 0.382 0.702298
LOC_361 -1.493e-01 1.648e-01 -0.906 0.364981
LOC_371 -1.811e-01 1.903e-01 -0.952 0.341153
LOC_381 -2.808e-01 4.466e-01 -0.629 0.529406
LOC_391 -5.041e-01 1.824e-01 -2.764 0.005708 **
LOC_401 -1.792e-01 2.185e-01 -0.820 0.412064
LOC_411 -1.674e-01 2.379e-01 -0.704 0.481595
LOC_421 -4.464e-01 1.702e-01 -2.623 0.008709 **
LOC_441 -6.328e-01 5.863e-01 -1.079 0.280484
LOC_451 -4.241e-01 2.848e-01 -1.489 0.136417
LOC_461 1.782e-01 3.416e-01 0.522 0.601878
LOC_471 -5.485e-01 2.576e-01 -2.129 0.033224 *
LOC_481 3.360e-01 1.064e-01 3.159 0.001581 **
LOC_491 -4.834e-01 2.534e-01 -1.907 0.056459 .
LOC_501 -1.231e+00 6.670e-01 -1.846 0.064920 .
LOC_511 -1.624e-01 9.230e-02 -1.759 0.078519 .
LOC_531 2.424e-01 1.553e-01 1.561 0.118452
LOC_541 -7.017e-01 3.438e-01 -2.041 0.041254 *
LOC_551 -8.288e-01 3.142e-01 -2.638 0.008331 **
TOA_151 -2.428e-03 7.029e-02 -0.035 0.972449
TOA_201 1.125e+00 1.938e-01 5.802 6.54e-09 ***
TOA_301 2.622e-01 8.984e-02 2.919 0.003515 **
TOA_321 3.993e-01 7.766e-01 0.514 0.607112
TOA_351 -7.729e-01 2.923e-01 -2.645 0.008180 **
TOA_381 3.477e-01 6.840e-02 5.083 3.72e-07 ***
TOA_401 -1.069e-01 1.765e-01 -0.606 0.544827
TOA_421 1.134e+00 4.412e-01 2.569 0.010193 *
TOA_441 1.973e+00 1.074e+00 1.837 0.066271 .
TOA_451 9.288e+00 1.373e+02 0.068 0.946067
PPGROUP_111 3.754e-01 1.468e-01 2.558 0.010542 *
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
(Dispersion parameter for binomial family taken to be 1)
Null deviance: 20683 on 14919 degrees of freedom
Residual deviance: 14278 on 14839 degrees of freedom
AIC: 14440
Number of Fisher Scoring iterations: 10
VIF
GSEGRD 6.325307
IndAvgSalary 7.356269
SalaryOverUnderIndAvg 1.389707
LowerLimitAge 2.380904
BLS_FEDERAL_OtherSep_Rate 10.826082
BLS_FEDERAL_Quits_Rate 4.183464
BLS_FEDERAL_OtherSep_Level 19.198400
BLS_FEDERAL_JobOpenings_Level 1.481026
BLS_FEDERAL_Layoffs_Rate 9.606578
BLS_FEDERAL_TotalSep_Rate 20.328387
LOSSqrt 1.885675
SEPCount_EFDATE_OCCLog 1.115541
SEPCount_EFDATE_LOCLog 13.944539
AGELVL_B 1.172784
AGELVL_C 1.658781
AGELVL_E 1.407933
AGELVL_F 1.285416
AGELVL_G 1.272661
AGELVL_H 1.362794
AGELVL_I 1.520713
LOC_01 1.804571
LOC_02 1.585242
LOC_04 1.803346
LOC_05 1.811844
LOC_06 1.739463
LOC_08 1.732912
LOC_09 1.724140
LOC_10 1.299397
LOC_12 1.593953
LOC_13 1.770420
LOC_15 2.113054
LOC_16 1.555436
LOC_17 1.751175
LOC_18 1.541328
LOC_19 1.603914
LOC_20 1.596656
LOC_21 1.696337
LOC_22 1.819867
LOC_23 1.620731
LOC_24 1.756401
LOC_25 1.753849
LOC_26 1.675057
LOC_27 1.568157
LOC_28 1.600069
LOC_29 1.437365
LOC_30 1.540415
LOC_31 1.490014
LOC_32 1.707584
LOC_33 1.336069
LOC_34 1.847719
LOC_35 2.028201
LOC_36 1.465620
LOC_37 1.666503
LOC_38 1.516084
LOC_39 1.744169
LOC_40 1.831185
LOC_41 1.516018
LOC_42 1.496738
LOC_44 1.372866
LOC_45 1.653945
LOC_46 1.788853
LOC_47 1.507251
LOC_48 1.595040
LOC_49 1.508619
LOC_50 1.296781
LOC_51 1.752218
LOC_53 1.593111
LOC_54 1.627931
LOC_55 1.587431
TOA_15 1.412211
TOA_20 1.029348
TOA_30 1.266604
TOA_32 1.006039
TOA_35 1.086640
TOA_38 1.179225
TOA_40 1.064876
TOA_42 1.011671
TOA_44 1.005267
TOA_45 1.000001
PPGROUP_11 1.742598
Removed BEFORE this step: BLS_FEDERAL_TotalSep_Rate
Call:
glm(formula = fla, family = "binomial", data = OPMAnalysisDataNoFamBinary)
Deviance Residuals:
Min 1Q Median 3Q Max
-2.9353 -0.7302 -0.1356 0.8100 3.2309
Coefficients:
Estimate Std. Error z value Pr(>|z|)
(Intercept) 3.674e+00 7.593e-01 4.839 1.30e-06 ***
GSEGRD -2.933e-01 3.058e-02 -9.591 < 2e-16 ***
IndAvgSalary 1.695e-05 1.888e-06 8.979 < 2e-16 ***
SalaryOverUnderIndAvg 2.796e-06 2.492e-06 1.122 0.261873
LowerLimitAge -4.835e-02 2.707e-03 -17.860 < 2e-16 ***
BLS_FEDERAL_OtherSep_Rate 9.281e+00 8.376e-01 11.080 < 2e-16 ***
BLS_FEDERAL_Quits_Rate 3.504e+00 5.004e-01 7.002 2.52e-12 ***
BLS_FEDERAL_OtherSep_Level -2.781e-01 3.715e-02 -7.484 7.19e-14 ***
BLS_FEDERAL_JobOpenings_Level 1.762e-02 2.113e-03 8.338 < 2e-16 ***
BLS_FEDERAL_Layoffs_Rate 5.677e-01 1.403e-01 4.046 5.21e-05 ***
LOSSqrt -7.880e-01 2.314e-02 -34.047 < 2e-16 ***
SEPCount_EFDATE_OCCLog -3.521e-02 1.623e-02 -2.170 0.029986 *
SEPCount_EFDATE_LOCLog -2.621e-01 9.394e-02 -2.791 0.005261 **
AGELVL_B1 -7.745e-01 2.237e-01 -3.461 0.000537 ***
AGELVL_C1 -1.638e-01 8.606e-02 -1.904 0.056965 .
AGELVL_E1 2.644e-01 6.777e-02 3.902 9.56e-05 ***
AGELVL_F1 4.790e-01 7.067e-02 6.779 1.21e-11 ***
AGELVL_G1 7.120e-01 7.434e-02 9.578 < 2e-16 ***
AGELVL_H1 8.553e-01 7.668e-02 11.154 < 2e-16 ***
AGELVL_I1 7.217e-01 8.141e-02 8.865 < 2e-16 ***
LOC_011 -4.892e-01 2.192e-01 -2.232 0.025638 *
LOC_021 -2.776e-01 3.149e-01 -0.882 0.377925
LOC_041 2.839e-01 1.761e-01 1.612 0.106953
LOC_051 -4.012e-01 3.127e-01 -1.283 0.199506
LOC_061 5.450e-01 1.040e-01 5.239 1.61e-07 ***
LOC_081 -2.602e-03 1.577e-01 -0.016 0.986836
LOC_091 -7.253e-01 4.261e-01 -1.702 0.088726 .
LOC_101 -6.300e-01 6.657e-01 -0.946 0.343986
LOC_121 -2.420e-02 1.414e-01 -0.171 0.864136
LOC_131 3.072e-02 1.451e-01 0.212 0.832282
LOC_151 -4.692e-01 2.275e-01 -2.063 0.039147 *
LOC_161 -4.091e-01 3.387e-01 -1.208 0.227138
LOC_171 -4.413e-01 1.862e-01 -2.370 0.017800 *
LOC_181 -6.293e-01 3.075e-01 -2.046 0.040713 *
LOC_191 -3.213e-01 4.049e-01 -0.794 0.427486
LOC_201 1.921e-01 3.208e-01 0.599 0.549229
LOC_211 -4.060e-01 2.617e-01 -1.551 0.120836
LOC_221 -5.259e-01 2.758e-01 -1.907 0.056546 .
LOC_231 -5.253e-01 4.323e-01 -1.215 0.224324
LOC_241 -1.295e-01 9.687e-02 -1.337 0.181206
LOC_251 -2.022e-01 2.357e-01 -0.858 0.391070
LOC_261 -4.227e-01 2.556e-01 -1.654 0.098182 .
LOC_271 -1.055e-01 2.964e-01 -0.356 0.722043
LOC_281 -4.927e-01 3.068e-01 -1.606 0.108340
LOC_291 -5.332e-01 1.989e-01 -2.681 0.007331 **
LOC_301 2.638e-01 2.955e-01 0.893 0.371852
LOC_311 -4.621e-01 4.082e-01 -1.132 0.257680
LOC_321 -2.290e-01 3.172e-01 -0.722 0.470470
LOC_331 -4.697e-01 6.027e-01 -0.779 0.435789
LOC_341 -5.750e-01 2.621e-01 -2.194 0.028237 *
LOC_351 1.570e-01 2.079e-01 0.755 0.450005
LOC_361 -1.381e-01 1.638e-01 -0.843 0.399268
LOC_371 -1.477e-01 1.901e-01 -0.777 0.437210
LOC_381 -1.724e-01 4.481e-01 -0.385 0.700499
LOC_391 -4.344e-01 1.815e-01 -2.393 0.016691 *
LOC_401 -1.196e-01 2.180e-01 -0.548 0.583434
LOC_411 -1.232e-01 2.376e-01 -0.518 0.604112
LOC_421 -4.013e-01 1.694e-01 -2.369 0.017822 *
LOC_441 -3.872e-01 5.858e-01 -0.661 0.508579
LOC_451 -3.294e-01 2.843e-01 -1.159 0.246639
LOC_461 2.401e-01 3.400e-01 0.706 0.480184
LOC_471 -4.740e-01 2.563e-01 -1.849 0.064421 .
LOC_481 3.338e-01 1.059e-01 3.151 0.001627 **
LOC_491 -4.321e-01 2.517e-01 -1.716 0.086092 .
LOC_501 -1.140e+00 6.713e-01 -1.698 0.089427 .
LOC_511 -1.533e-01 9.194e-02 -1.668 0.095375 .
LOC_531 2.774e-01 1.551e-01 1.789 0.073685 .
LOC_541 -5.950e-01 3.405e-01 -1.748 0.080527 .
LOC_551 -7.583e-01 3.127e-01 -2.425 0.015318 *
TOA_151 -1.144e-02 7.005e-02 -0.163 0.870254
TOA_201 1.091e+00 1.938e-01 5.631 1.79e-08 ***
TOA_301 2.580e-01 8.931e-02 2.889 0.003869 **
TOA_321 3.043e-01 7.653e-01 0.398 0.690928
TOA_351 -7.314e-01 2.925e-01 -2.501 0.012395 *
TOA_381 3.532e-01 6.815e-02 5.182 2.20e-07 ***
TOA_401 -1.094e-01 1.758e-01 -0.622 0.533864
TOA_421 1.141e+00 4.406e-01 2.590 0.009598 **
TOA_441 1.923e+00 1.075e+00 1.789 0.073605 .
TOA_451 9.298e+00 1.390e+02 0.067 0.946684
PPGROUP_111 3.646e-01 1.463e-01 2.491 0.012722 *
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
(Dispersion parameter for binomial family taken to be 1)
Null deviance: 20683 on 14919 degrees of freedom
Residual deviance: 14388 on 14840 degrees of freedom
AIC: 14548
Number of Fisher Scoring iterations: 10
VIF
GSEGRD 6.341945
IndAvgSalary 7.370123
SalaryOverUnderIndAvg 1.389753
LowerLimitAge 2.376196
BLS_FEDERAL_OtherSep_Rate 10.825917
BLS_FEDERAL_Quits_Rate 3.044004
BLS_FEDERAL_OtherSep_Level 17.475471
BLS_FEDERAL_JobOpenings_Level 1.407951
BLS_FEDERAL_Layoffs_Rate 1.799169
LOSSqrt 1.879479
SEPCount_EFDATE_OCCLog 1.115124
SEPCount_EFDATE_LOCLog 13.998472
AGELVL_B 1.174410
AGELVL_C 1.657234
AGELVL_E 1.408689
AGELVL_F 1.284917
AGELVL_G 1.273502
AGELVL_H 1.363176
AGELVL_I 1.520633
LOC_01 1.811694
LOC_02 1.585316
LOC_04 1.800230
LOC_05 1.819679
LOC_06 1.738628
LOC_08 1.736083
LOC_09 1.726487
LOC_10 1.303526
LOC_12 1.591695
LOC_13 1.779967
LOC_15 2.118655
LOC_16 1.562957
LOC_17 1.756577
LOC_18 1.534246
LOC_19 1.596140
LOC_20 1.595951
LOC_21 1.703234
LOC_22 1.817510
LOC_23 1.628642
LOC_24 1.762368
LOC_25 1.760211
LOC_26 1.678602
LOC_27 1.583312
LOC_28 1.604592
LOC_29 1.443580
LOC_30 1.539174
LOC_31 1.491019
LOC_32 1.718984
LOC_33 1.340572
LOC_34 1.852480
LOC_35 2.032909
LOC_36 1.473198
LOC_37 1.665442
LOC_38 1.508745
LOC_39 1.748613
LOC_40 1.834955
LOC_41 1.517115
LOC_42 1.500269
LOC_44 1.370811
LOC_45 1.653431
LOC_46 1.798056
LOC_47 1.514145
LOC_48 1.593242
LOC_49 1.518887
LOC_50 1.291666
LOC_51 1.752969
LOC_53 1.594601
LOC_54 1.645998
LOC_55 1.592307
TOA_15 1.409587
TOA_20 1.028883
TOA_30 1.267760
TOA_32 1.006042
TOA_35 1.086763
TOA_38 1.179202
TOA_40 1.064819
TOA_42 1.011394
TOA_44 1.005168
TOA_45 1.000001
PPGROUP_11 1.741409
Removed BEFORE this step: BLS_FEDERAL_OtherSep_Level
Call:
glm(formula = fla, family = "binomial", data = OPMAnalysisDataNoFamBinary)
Deviance Residuals:
Min 1Q Median 3Q Max
-2.9532 -0.7383 -0.1365 0.8198 3.1762
Coefficients:
Estimate Std. Error z value Pr(>|z|)
(Intercept) 5.287e+00 7.319e-01 7.224 5.06e-13 ***
GSEGRD -2.922e-01 3.051e-02 -9.579 < 2e-16 ***
IndAvgSalary 1.699e-05 1.884e-06 9.019 < 2e-16 ***
SalaryOverUnderIndAvg 3.106e-06 2.489e-06 1.248 0.211960
LowerLimitAge -4.802e-02 2.703e-03 -17.763 < 2e-16 ***
BLS_FEDERAL_OtherSep_Rate 3.587e+00 3.444e-01 10.416 < 2e-16 ***
BLS_FEDERAL_Quits_Rate 6.748e-01 3.255e-01 2.073 0.038190 *
BLS_FEDERAL_JobOpenings_Level 1.429e-02 2.067e-03 6.911 4.82e-12 ***
BLS_FEDERAL_Layoffs_Rate 8.280e-02 1.245e-01 0.665 0.506114
LOSSqrt -7.882e-01 2.310e-02 -34.119 < 2e-16 ***
SEPCount_EFDATE_OCCLog -3.684e-02 1.620e-02 -2.274 0.022949 *
SEPCount_EFDATE_LOCLog -3.727e-01 9.321e-02 -3.999 6.36e-05 ***
AGELVL_B1 -7.646e-01 2.235e-01 -3.421 0.000624 ***
AGELVL_C1 -1.656e-01 8.580e-02 -1.930 0.053636 .
AGELVL_E1 2.686e-01 6.757e-02 3.975 7.04e-05 ***
AGELVL_F1 4.737e-01 7.044e-02 6.724 1.77e-11 ***
AGELVL_G1 7.031e-01 7.420e-02 9.475 < 2e-16 ***
AGELVL_H1 8.441e-01 7.650e-02 11.034 < 2e-16 ***
AGELVL_I1 7.181e-01 8.133e-02 8.829 < 2e-16 ***
LOC_011 -6.540e-01 2.187e-01 -2.991 0.002784 **
LOC_021 -4.875e-01 3.135e-01 -1.555 0.119925
LOC_041 1.660e-01 1.753e-01 0.947 0.343661
LOC_051 -6.508e-01 3.112e-01 -2.091 0.036505 *
LOC_061 5.681e-01 1.038e-01 5.473 4.42e-08 ***
LOC_081 -1.113e-01 1.572e-01 -0.708 0.479025
LOC_091 -1.049e+00 4.271e-01 -2.457 0.014018 *
LOC_101 -9.956e-01 6.615e-01 -1.505 0.132320
LOC_121 -9.499e-02 1.412e-01 -0.673 0.500990
LOC_131 -6.792e-02 1.445e-01 -0.470 0.638345
LOC_151 -6.324e-01 2.262e-01 -2.796 0.005168 **
LOC_161 -6.305e-01 3.373e-01 -1.869 0.061607 .
LOC_171 -5.595e-01 1.854e-01 -3.017 0.002550 **
LOC_181 -8.125e-01 3.072e-01 -2.644 0.008182 **
LOC_191 -6.385e-01 4.020e-01 -1.588 0.112244
LOC_201 -3.906e-02 3.195e-01 -0.122 0.902696
LOC_211 -5.910e-01 2.609e-01 -2.265 0.023522 *
LOC_221 -7.343e-01 2.749e-01 -2.671 0.007554 **
LOC_231 -8.506e-01 4.285e-01 -1.985 0.047143 *
LOC_241 -1.614e-01 9.661e-02 -1.671 0.094767 .
LOC_251 -3.625e-01 2.351e-01 -1.542 0.123038
LOC_261 -6.001e-01 2.544e-01 -2.359 0.018345 *
LOC_271 -3.339e-01 2.949e-01 -1.132 0.257591
LOC_281 -7.362e-01 3.052e-01 -2.412 0.015867 *
LOC_291 -6.371e-01 1.987e-01 -3.206 0.001344 **
LOC_301 7.847e-02 2.940e-01 0.267 0.789516
LOC_311 -7.103e-01 4.067e-01 -1.747 0.080684 .
LOC_321 -4.641e-01 3.165e-01 -1.466 0.142534
LOC_331 -8.586e-01 5.959e-01 -1.441 0.149650
LOC_341 -7.725e-01 2.604e-01 -2.967 0.003006 **
LOC_351 -3.262e-03 2.072e-01 -0.016 0.987437
LOC_361 -2.407e-01 1.634e-01 -1.473 0.140694
LOC_371 -2.692e-01 1.896e-01 -1.420 0.155614
LOC_381 -4.733e-01 4.474e-01 -1.058 0.290112
LOC_391 -5.567e-01 1.804e-01 -3.086 0.002030 **
LOC_401 -2.741e-01 2.176e-01 -1.260 0.207833
LOC_411 -2.777e-01 2.369e-01 -1.172 0.241023
LOC_421 -4.883e-01 1.689e-01 -2.892 0.003829 **
LOC_441 -7.413e-01 5.787e-01 -1.281 0.200190
LOC_451 -5.634e-01 2.829e-01 -1.992 0.046409 *
LOC_461 -3.944e-02 3.376e-01 -0.117 0.906993
LOC_471 -6.524e-01 2.550e-01 -2.558 0.010530 *
LOC_481 3.332e-01 1.056e-01 3.156 0.001599 **
LOC_491 -5.904e-01 2.506e-01 -2.356 0.018468 *
LOC_501 -1.484e+00 6.731e-01 -2.206 0.027418 *
LOC_511 -1.649e-01 9.177e-02 -1.797 0.072338 .
LOC_531 1.878e-01 1.543e-01 1.217 0.223567
LOC_541 -8.106e-01 3.388e-01 -2.393 0.016726 *
LOC_551 -9.633e-01 3.115e-01 -3.092 0.001985 **
TOA_151 -7.958e-03 6.983e-02 -0.114 0.909269
TOA_201 1.108e+00 1.936e-01 5.725 1.04e-08 ***
TOA_301 2.554e-01 8.920e-02 2.863 0.004190 **
TOA_321 3.097e-01 7.628e-01 0.406 0.684736
TOA_351 -7.413e-01 2.919e-01 -2.539 0.011107 *
TOA_381 3.605e-01 6.796e-02 5.304 1.13e-07 ***
TOA_401 -9.923e-02 1.751e-01 -0.567 0.570842
TOA_421 1.154e+00 4.401e-01 2.621 0.008761 **
TOA_441 1.955e+00 1.074e+00 1.820 0.068706 .
TOA_451 9.497e+00 1.389e+02 0.068 0.945507
PPGROUP_111 3.845e-01 1.460e-01 2.634 0.008441 **
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
(Dispersion parameter for binomial family taken to be 1)
Null deviance: 20683 on 14919 degrees of freedom
Residual deviance: 14445 on 14841 degrees of freedom
AIC: 14603
Number of Fisher Scoring iterations: 10
VIF
GSEGRD 6.340938
IndAvgSalary 7.356814
SalaryOverUnderIndAvg 1.391728
LowerLimitAge 2.376786
BLS_FEDERAL_OtherSep_Rate 1.837442
BLS_FEDERAL_Quits_Rate 1.304298
BLS_FEDERAL_JobOpenings_Level 1.352153
BLS_FEDERAL_Layoffs_Rate 1.426971
LOSSqrt 1.876778
SEPCount_EFDATE_OCCLog 1.114597
SEPCount_EFDATE_LOCLog 13.850861
AGELVL_B 1.173733
AGELVL_C 1.657051
AGELVL_E 1.408001
AGELVL_F 1.285024
AGELVL_G 1.273653
AGELVL_H 1.364834
AGELVL_I 1.522927
LOC_01 1.801446
LOC_02 1.579994
LOC_04 1.797805
LOC_05 1.813437
LOC_06 1.736764
LOC_08 1.729533
LOC_09 1.703796
LOC_10 1.302073
LOC_12 1.587882
LOC_13 1.771815
LOC_15 2.115854
LOC_16 1.564891
LOC_17 1.756185
LOC_18 1.527946
LOC_19 1.593072
LOC_20 1.588630
LOC_21 1.697363
LOC_22 1.808539
LOC_23 1.628683
LOC_24 1.763482
LOC_25 1.755846
LOC_26 1.675467
LOC_27 1.575193
LOC_28 1.596194
LOC_29 1.441176
LOC_30 1.531651
LOC_31 1.488323
LOC_32 1.704091
LOC_33 1.342222
LOC_34 1.854575
LOC_35 2.022229
LOC_36 1.466953
LOC_37 1.660334
LOC_38 1.501093
LOC_39 1.748446
LOC_40 1.825332
LOC_41 1.510419
LOC_42 1.498880
LOC_44 1.375688
LOC_45 1.641557
LOC_46 1.791183
LOC_47 1.510186
LOC_48 1.594814
LOC_49 1.514824
LOC_50 1.285312
LOC_51 1.752417
LOC_53 1.592484
LOC_54 1.649127
LOC_55 1.590688
TOA_15 1.409930
TOA_20 1.028661
TOA_30 1.269052
TOA_32 1.006115
TOA_35 1.085718
TOA_38 1.178584
TOA_40 1.065235
TOA_42 1.011066
TOA_44 1.005141
TOA_45 1.000001
PPGROUP_11 1.732739
Removed BEFORE this step: SEPCount_EFDATE_LOCLog
Call:
glm(formula = fla, family = "binomial", data = OPMAnalysisDataNoFamBinary)
Deviance Residuals:
Min 1Q Median 3Q Max
-2.9404 -0.7343 -0.1383 0.8212 3.1759
Coefficients:
Estimate Std. Error z value Pr(>|z|)
(Intercept) 2.745e+00 3.596e-01 7.635 2.26e-14 ***
GSEGRD -2.950e-01 3.049e-02 -9.675 < 2e-16 ***
IndAvgSalary 1.689e-05 1.882e-06 8.974 < 2e-16 ***
SalaryOverUnderIndAvg 2.876e-06 2.485e-06 1.158 0.247034
LowerLimitAge -4.756e-02 2.697e-03 -17.631 < 2e-16 ***
BLS_FEDERAL_OtherSep_Rate 3.346e+00 3.388e-01 9.875 < 2e-16 ***
BLS_FEDERAL_Quits_Rate 3.838e-01 3.169e-01 1.211 0.225844
BLS_FEDERAL_JobOpenings_Level 1.720e-02 1.938e-03 8.877 < 2e-16 ***
BLS_FEDERAL_Layoffs_Rate 1.669e-02 1.233e-01 0.135 0.892372
LOSSqrt -7.857e-01 2.306e-02 -34.069 < 2e-16 ***
SEPCount_EFDATE_OCCLog -3.844e-02 1.618e-02 -2.376 0.017523 *
AGELVL_B1 -7.652e-01 2.235e-01 -3.424 0.000618 ***
AGELVL_C1 -1.649e-01 8.578e-02 -1.923 0.054507 .
AGELVL_E1 2.708e-01 6.752e-02 4.011 6.06e-05 ***
AGELVL_F1 4.758e-01 7.038e-02 6.759 1.38e-11 ***
AGELVL_G1 7.042e-01 7.414e-02 9.498 < 2e-16 ***
AGELVL_H1 8.438e-01 7.643e-02 11.040 < 2e-16 ***
AGELVL_I1 7.158e-01 8.128e-02 8.807 < 2e-16 ***
LOC_011 -1.307e-01 1.749e-01 -0.748 0.454749
LOC_021 2.189e-01 2.586e-01 0.846 0.397322
LOC_041 5.389e-01 1.483e-01 3.634 0.000279 ***
LOC_051 1.374e-01 2.405e-01 0.571 0.567951
LOC_061 4.687e-01 1.007e-01 4.654 3.25e-06 ***
LOC_081 2.092e-01 1.350e-01 1.549 0.121329
LOC_091 1.354e-02 3.349e-01 0.040 0.967746
LOC_101 2.475e-01 5.805e-01 0.426 0.669886
LOC_121 1.287e-01 1.294e-01 0.995 0.319910
LOC_131 2.155e-01 1.257e-01 1.714 0.086450 .
LOC_151 -2.843e-02 1.684e-01 -0.169 0.865939
LOC_161 1.264e-01 2.783e-01 0.454 0.649756
LOC_171 -1.401e-01 1.526e-01 -0.918 0.358403
LOC_181 -1.428e-01 2.571e-01 -0.555 0.578769
LOC_191 3.025e-01 3.269e-01 0.926 0.354679
LOC_201 6.877e-01 2.626e-01 2.619 0.008813 **
LOC_211 2.026e-02 2.112e-01 0.096 0.923577
LOC_221 -4.942e-02 2.147e-01 -0.230 0.817933
LOC_231 1.774e-01 3.423e-01 0.518 0.604303
LOC_241 -6.758e-02 9.365e-02 -0.722 0.470546
LOC_251 2.047e-01 1.870e-01 1.095 0.273489
LOC_261 -1.108e-02 2.070e-01 -0.054 0.957324
LOC_271 3.257e-01 2.446e-01 1.332 0.182939
LOC_281 -4.111e-02 2.509e-01 -0.164 0.869845
LOC_291 -2.891e-01 1.781e-01 -1.624 0.104428
LOC_301 7.125e-01 2.483e-01 2.869 0.004112 **
LOC_311 1.772e-01 3.401e-01 0.521 0.602346
LOC_321 3.018e-01 2.520e-01 1.198 0.230974
LOC_331 3.095e-01 5.209e-01 0.594 0.552413
LOC_341 -1.125e-01 2.008e-01 -0.560 0.575224
LOC_351 5.158e-01 1.615e-01 3.193 0.001407 **
LOC_361 3.142e-02 1.484e-01 0.212 0.832277
LOC_371 1.303e-01 1.608e-01 0.810 0.417993
LOC_381 5.196e-01 3.731e-01 1.393 0.163639
LOC_391 -1.624e-01 1.508e-01 -1.077 0.281509
LOC_401 2.483e-01 1.738e-01 1.428 0.153223
LOC_411 2.023e-01 2.037e-01 0.993 0.320573
LOC_421 -1.907e-01 1.513e-01 -1.260 0.207503
LOC_441 4.373e-01 4.973e-01 0.879 0.379227
LOC_451 8.970e-02 2.304e-01 0.389 0.696994
LOC_461 8.073e-01 2.631e-01 3.069 0.002149 **
LOC_471 -1.224e-01 2.173e-01 -0.563 0.573384
LOC_481 3.287e-01 1.055e-01 3.117 0.001830 **
LOC_491 -7.061e-02 2.145e-01 -0.329 0.741976
LOC_501 -2.519e-01 5.972e-01 -0.422 0.673226
LOC_511 -1.460e-01 9.164e-02 -1.593 0.111186
LOC_531 4.629e-01 1.379e-01 3.357 0.000787 ***
LOC_541 -1.260e-03 2.717e-01 -0.005 0.996301
LOC_551 -2.568e-01 2.565e-01 -1.001 0.316627
TOA_151 -9.509e-03 6.981e-02 -0.136 0.891646
TOA_201 1.114e+00 1.935e-01 5.756 8.60e-09 ***
TOA_301 2.531e-01 8.918e-02 2.838 0.004544 **
TOA_321 3.369e-01 7.593e-01 0.444 0.657309
TOA_351 -7.563e-01 2.916e-01 -2.594 0.009496 **
TOA_381 3.576e-01 6.789e-02 5.268 1.38e-07 ***
TOA_401 -1.062e-01 1.750e-01 -0.607 0.543981
TOA_421 1.145e+00 4.400e-01 2.603 0.009241 **
TOA_441 1.927e+00 1.073e+00 1.795 0.072617 .
TOA_451 9.465e+00 1.390e+02 0.068 0.945714
PPGROUP_111 3.738e-01 1.457e-01 2.565 0.010330 *
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
(Dispersion parameter for binomial family taken to be 1)
Null deviance: 20683 on 14919 degrees of freedom
Residual deviance: 14461 on 14842 degrees of freedom
AIC: 14617
Number of Fisher Scoring iterations: 10
VIF
GSEGRD 6.336016
IndAvgSalary 7.358771
SalaryOverUnderIndAvg 1.389651
LowerLimitAge 2.369312
BLS_FEDERAL_OtherSep_Rate 1.782477
BLS_FEDERAL_Quits_Rate 1.239868
BLS_FEDERAL_JobOpenings_Level 1.188769
BLS_FEDERAL_Layoffs_Rate 1.402466
LOSSqrt 1.872374
SEPCount_EFDATE_OCCLog 1.114156
AGELVL_B 1.174018
AGELVL_C 1.657118
AGELVL_E 1.408279
AGELVL_F 1.284967
AGELVL_G 1.273409
AGELVL_H 1.363993
AGELVL_I 1.521333
LOC_01 1.155460
LOC_02 1.076294
LOC_04 1.288291
LOC_05 1.084919
LOC_06 1.638853
LOC_08 1.279200
LOC_09 1.043171
LOC_10 1.015127
LOC_12 1.338156
LOC_13 1.344786
LOC_15 1.170698
LOC_16 1.069523
LOC_17 1.192634
LOC_18 1.072685
LOC_19 1.046542
LOC_20 1.074673
LOC_21 1.113178
LOC_22 1.104323
LOC_23 1.041955
LOC_24 1.658068
LOC_25 1.115819
LOC_26 1.112352
LOC_27 1.080944
LOC_28 1.077166
LOC_29 1.163211
LOC_30 1.089820
LOC_31 1.044267
LOC_32 1.078497
LOC_33 1.019745
LOC_34 1.107036
LOC_35 1.228142
LOC_36 1.211008
LOC_37 1.197596
LOC_38 1.038371
LOC_39 1.225100
LOC_40 1.165699
LOC_41 1.121614
LOC_42 1.206579
LOC_44 1.018926
LOC_45 1.092520
LOC_46 1.085858
LOC_47 1.101845
LOC_48 1.596293
LOC_49 1.105336
LOC_50 1.015236
LOC_51 1.746972
LOC_53 1.275738
LOC_54 1.059452
LOC_55 1.076786
TOA_15 1.409602
TOA_20 1.028455
TOA_30 1.268027
TOA_32 1.006043
TOA_35 1.086146
TOA_38 1.178145
TOA_40 1.064906
TOA_42 1.011026
TOA_44 1.005096
TOA_45 1.000001
PPGROUP_11 1.734680
Following variables removed based on VIF values (in order of removal):
[1] "BLS_FEDERAL_JobOpenings_Rate" "BLS_FEDERAL_TotalSep_Level"
[3] "BLS_FEDERAL_Layoffs_Level" "AGELVL_D"
[5] "IndAvgSalaryLog" "BLS_FEDERAL_Quits_Level"
[7] "LOC_11" "SALARYLog"
[9] "TOA_10" "BLS_FEDERAL_TotalSep_Rate"
[11] "BLS_FEDERAL_OtherSep_Level" "SEPCount_EFDATE_LOCLog"
Null Deviances (in order):
[1] "7226.05152246447" "7219.36370064357" "6767.88407188207" "6767.82808686825"
[5] "6742.92826531162" "6473.82478843413" "6471.71501030948" "6436.43729999307"
[9] "6405.40525464846" "6294.74008501623" "6238.61063516369" "6222.45281211915"
Min value at iteration = 12
Diff Degrees of Freedom (in order):
[1] "88" "87" "86" "85" "84" "83" "82" "81" "80" "79" "78" "77"
Min value at iteration = 12
Log Likelihoods (in order):
[1] "-6728.54665990542" "-6731.89057081586" "-6957.63038519662"
[4] "-6957.65837770352" "-6970.10828848184" "-7104.66002692059"
[7] "-7105.71491598291" "-7123.35377114112" "-7138.86979381342"
[10] "-7194.20237862954" "-7222.2671035558" "-7230.34601507808"
Min value at iteration = 12
AIC values (in order):
[1] "13635.0933198108" "13639.7811416317" "14089.2607703932" "14087.316755407"
[5] "14110.2165769637" "14377.3200538412" "14377.4298319658" "14410.7075422822"
[9] "14439.7395876268" "14548.4047572591" "14602.5342071116" "14616.6920301562"
Min value at iteration = 1
BIC values (in order):
[1] "14312.4240705753" "14309.5014345224" "14751.3706054102" "14741.8161325502"
[5] "14757.1054962331" "15016.5985152368" "15009.0978354877" "15034.7650879304"
[9] "15056.1866754012" "15157.2413871597" "15203.7603791385" "15210.3077443093"
Min value at iteration = 2
%%R
#vars.Repeat <- replace(vars.Repeat, vars.Repeat == "IndAvgSalary", "IndAvgSalaryLog")
vars.Repeat <- vars.Repeat[!vars.Repeat %in% c("IndAvgSalary", "IndAvgSalaryLog")]
%%R
##data.frame(summary(BinLogit)$coef[summary(BinLogit)$coef[,4] <= .05, 4]) #Review coefficients of p-value less than 0.05
#LogitCoeffs <- data.frame(summary(BinLogit.P.Repeat)$coef[-1,4]) #Ignore Intercept and only look at p-values
##LogitCoeffs[LogitCoeffs$`summary.BinLogit..coef..1..4.` == max(LogitCoeffs$`summary.BinLogit..coef..1..4.`),]
#maxP <- cbind(rownames(LogitCoeffs)[LogitCoeffs$`summary.BinLogit.P.Repeat..coef..1..4.` == max(LogitCoeffs$`summary.BinLogit.P.Repeat..coef..1..4.`)],
# max(LogitCoeffs))
#maxP[1,1]
varsP.Repeat <- vars.Repeat
P.removed <- vector(mode="character", length=0)
ndev.vect <- vector(mode="character", length=0)
ndf.vect <- vector(mode="character", length=0)
pchisq.vect <- vector(mode="character", length=0)
logLik.vect <- vector(mode="character", length=0)
AIC.vect <- vector(mode="character", length=0)
BIC.vect <- vector(mode="character", length=0)
for(i in seq(1,45)){
BinLogit.P.Repeat <- runLogit(remove, varsP.Repeat)
print(summary(BinLogit.P.Repeat))
LogitCoeffs <- data.frame(summary(BinLogit.P.Repeat)$coef[-1,4]) #Ignore Intercept and only look at p-values
maxP <- cbind(rownames(LogitCoeffs)[LogitCoeffs$`summary.BinLogit.P.Repeat..coef..1..4.` == max(LogitCoeffs$`summary.BinLogit.P.Repeat..coef..1..4.`)],
max(LogitCoeffs))
vifs.BinLogitP.Repeat <- runVifs(BinLogit.P.Repeat)
remove <- maxP[1,1]
remove <- ifelse(grepl('[[:digit:]]$', remove), substr(remove, 1, nchar(remove)-1), remove)
P.removed <- c(P.removed, remove)
cat("\nRemoved AFTER this step:", remove, "\n\n\n")
varsP.Repeat <- varsP.Repeat[!(varsP.Repeat %in% c(remove))]
##goodness of fit
ndev.vect <- c(ndev.vect, with(BinLogit.P.Repeat, null.deviance - deviance))
ndf.vect <- c(ndf.vect, with(BinLogit.P.Repeat, df.null - df.residual))
pchisq.vect <- c(pchisq.vect, with(BinLogit.P.Repeat, pchisq(null.deviance - deviance, df.null - df.residual, lower.tail = FALSE)))
logLik.vect <- c(logLik.vect, logLik(BinLogit.P.Repeat))
AIC.vect <- c(AIC.vect, AIC(BinLogit.P.Repeat))
BIC.vect <- c(BIC.vect, BIC(BinLogit.P.Repeat))
}
cat("\nFollowing variables removed based on p-values (in order of removal):\n")
print(P.removed)
cat("\n\nNull Deviances (in order):\n")
print(ndev.vect)
cat("\nMin value at iteration = ", which.min(ndev.vect))
cat("\n\nDiff Degrees of Freedom (in order):\n")
print(ndf.vect)
cat("\nMin value at iteration = ", which.min(ndf.vect))
#cat("\n\nP-ChiSquare (in order):\n")
#print(pchisq.vect)
#cat("\nMin value at iteration = ", which.min(pchisq.vect))
cat("\n\nLog Likelihoods (in order):\n")
print(logLik.vect)
cat("\nMin value at iteration = ", which.min(logLik.vect))
cat("\n\nAIC values (in order):\n")
print(AIC.vect)
cat("\nMin value at iteration = ", which.min(AIC.vect))
cat("\n\nBIC values (in order):\n")
print(BIC.vect)
cat("\nMin value at iteration = ", which.min(BIC.vect))
Call:
glm(formula = fla, family = "binomial", data = OPMAnalysisDataNoFamBinary)
Deviance Residuals:
Min 1Q Median 3Q Max
-2.8257 -0.7389 -0.1369 0.8192 3.2349
Coefficients:
Estimate Std. Error z value Pr(>|z|)
(Intercept) 2.164e+00 3.501e-01 6.180 6.40e-10 ***
GSEGRD -6.306e-02 1.543e-02 -4.086 4.38e-05 ***
SalaryOverUnderIndAvg 8.942e-07 2.407e-06 0.371 0.710304
LowerLimitAge -4.492e-02 2.664e-03 -16.863 < 2e-16 ***
BLS_FEDERAL_OtherSep_Rate 3.241e+00 3.376e-01 9.600 < 2e-16 ***
BLS_FEDERAL_Quits_Rate 4.739e-01 3.158e-01 1.500 0.133506
BLS_FEDERAL_JobOpenings_Level 1.707e-02 1.931e-03 8.841 < 2e-16 ***
BLS_FEDERAL_Layoffs_Rate 4.260e-02 1.230e-01 0.346 0.729124
LOSSqrt -7.824e-01 2.293e-02 -34.117 < 2e-16 ***
SEPCount_EFDATE_OCCLog -3.092e-02 1.612e-02 -1.919 0.055040 .
AGELVL_B1 -5.865e-01 2.200e-01 -2.666 0.007681 **
AGELVL_C1 -1.685e-01 8.534e-02 -1.974 0.048329 *
AGELVL_E1 2.740e-01 6.732e-02 4.071 4.69e-05 ***
AGELVL_F1 4.894e-01 7.015e-02 6.976 3.04e-12 ***
AGELVL_G1 7.122e-01 7.401e-02 9.623 < 2e-16 ***
AGELVL_H1 8.467e-01 7.623e-02 11.106 < 2e-16 ***
AGELVL_I1 7.176e-01 8.111e-02 8.847 < 2e-16 ***
LOC_011 -2.079e-01 1.746e-01 -1.191 0.233732
LOC_021 1.563e-01 2.560e-01 0.611 0.541422
LOC_041 5.289e-01 1.471e-01 3.595 0.000325 ***
LOC_051 8.352e-02 2.384e-01 0.350 0.726039
LOC_061 4.123e-01 1.007e-01 4.096 4.21e-05 ***
LOC_081 1.457e-01 1.347e-01 1.081 0.279502
LOC_091 -5.574e-02 3.326e-01 -0.168 0.866909
LOC_101 1.984e-01 5.833e-01 0.340 0.733717
LOC_121 3.653e-02 1.292e-01 0.283 0.777292
LOC_131 1.713e-01 1.257e-01 1.363 0.172839
LOC_151 -6.696e-02 1.679e-01 -0.399 0.690072
LOC_161 5.430e-02 2.754e-01 0.197 0.843696
LOC_171 -2.199e-01 1.517e-01 -1.450 0.147157
LOC_181 -2.260e-01 2.553e-01 -0.885 0.375953
LOC_191 2.173e-01 3.232e-01 0.672 0.501296
LOC_201 6.153e-01 2.607e-01 2.360 0.018276 *
LOC_211 -2.959e-02 2.090e-01 -0.142 0.887417
LOC_221 -1.098e-01 2.132e-01 -0.515 0.606432
LOC_231 7.089e-02 3.402e-01 0.208 0.834951
LOC_241 -1.077e-01 9.422e-02 -1.144 0.252826
LOC_251 1.454e-01 1.869e-01 0.778 0.436632
LOC_261 -7.013e-02 2.065e-01 -0.340 0.734203
LOC_271 2.898e-01 2.424e-01 1.196 0.231868
LOC_281 -1.234e-01 2.506e-01 -0.492 0.622505
LOC_291 -3.485e-01 1.763e-01 -1.978 0.047985 *
LOC_301 6.821e-01 2.453e-01 2.781 0.005425 **
LOC_311 1.345e-01 3.381e-01 0.398 0.690894
LOC_321 2.332e-01 2.485e-01 0.939 0.347934
LOC_331 1.992e-01 5.206e-01 0.383 0.701933
LOC_341 -1.807e-01 2.003e-01 -0.902 0.367024
LOC_351 4.741e-01 1.597e-01 2.968 0.003000 **
LOC_361 -2.760e-02 1.483e-01 -0.186 0.852409
LOC_371 6.922e-02 1.600e-01 0.433 0.665374
LOC_381 4.903e-01 3.698e-01 1.326 0.184807
LOC_391 -2.892e-01 1.504e-01 -1.922 0.054549 .
LOC_401 1.795e-01 1.726e-01 1.040 0.298214
LOC_411 1.169e-01 2.027e-01 0.577 0.563965
LOC_421 -2.936e-01 1.508e-01 -1.947 0.051510 .
LOC_441 3.399e-01 4.936e-01 0.689 0.490990
LOC_451 6.030e-02 2.297e-01 0.263 0.792925
LOC_461 8.158e-01 2.590e-01 3.149 0.001636 **
LOC_471 -1.775e-01 2.153e-01 -0.824 0.409699
LOC_481 2.529e-01 1.052e-01 2.404 0.016200 *
LOC_491 -1.834e-01 2.140e-01 -0.857 0.391511
LOC_501 -4.076e-01 5.947e-01 -0.685 0.493070
LOC_511 -1.541e-01 9.176e-02 -1.679 0.093158 .
LOC_531 3.993e-01 1.373e-01 2.909 0.003627 **
LOC_541 -6.049e-02 2.704e-01 -0.224 0.823000
LOC_551 -3.197e-01 2.548e-01 -1.254 0.209681
TOA_151 6.868e-02 6.912e-02 0.994 0.320443
TOA_201 1.128e+00 1.928e-01 5.850 4.91e-09 ***
TOA_301 3.464e-01 8.928e-02 3.880 0.000104 ***
TOA_321 4.203e-01 7.540e-01 0.557 0.577220
TOA_351 -5.348e-01 2.867e-01 -1.865 0.062116 .
TOA_381 3.287e-01 6.730e-02 4.884 1.04e-06 ***
TOA_401 -1.054e-01 1.765e-01 -0.597 0.550292
TOA_421 1.141e+00 4.414e-01 2.585 0.009751 **
TOA_441 2.165e+00 1.073e+00 2.018 0.043574 *
TOA_451 9.288e+00 1.390e+02 0.067 0.946731
PPGROUP_111 -4.096e-01 1.172e-01 -3.494 0.000477 ***
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
(Dispersion parameter for binomial family taken to be 1)
Null deviance: 20683 on 14919 degrees of freedom
Residual deviance: 14543 on 14843 degrees of freedom
AIC: 14697
Number of Fisher Scoring iterations: 10
VIF
GSEGRD 1.709771
SalaryOverUnderIndAvg 1.362063
LowerLimitAge 2.331432
BLS_FEDERAL_OtherSep_Rate 1.780588
BLS_FEDERAL_Quits_Rate 1.239370
BLS_FEDERAL_JobOpenings_Level 1.189501
BLS_FEDERAL_Layoffs_Rate 1.400736
LOSSqrt 1.863535
SEPCount_EFDATE_OCCLog 1.110247
AGELVL_B 1.168836
AGELVL_C 1.661736
AGELVL_E 1.407665
AGELVL_F 1.284149
AGELVL_G 1.271136
AGELVL_H 1.360827
AGELVL_I 1.516939
LOC_01 1.155470
LOC_02 1.077857
LOC_04 1.298072
LOC_05 1.086904
LOC_06 1.643628
LOC_08 1.281373
LOC_09 1.043573
LOC_10 1.015121
LOC_12 1.333413
LOC_13 1.347233
LOC_15 1.173350
LOC_16 1.070565
LOC_17 1.194540
LOC_18 1.073599
LOC_19 1.046991
LOC_20 1.074966
LOC_21 1.115080
LOC_22 1.105564
LOC_23 1.041794
LOC_24 1.659934
LOC_25 1.116609
LOC_26 1.113438
LOC_27 1.083260
LOC_28 1.076607
LOC_29 1.166602
LOC_30 1.092269
LOC_31 1.044699
LOC_32 1.080552
LOC_33 1.019291
LOC_34 1.108298
LOC_35 1.236012
LOC_36 1.212675
LOC_37 1.199467
LOC_38 1.038974
LOC_39 1.218402
LOC_40 1.167278
LOC_41 1.121708
LOC_42 1.204208
LOC_44 1.018967
LOC_45 1.093473
LOC_46 1.088792
LOC_47 1.104474
LOC_48 1.598139
LOC_49 1.103074
LOC_50 1.014634
LOC_51 1.786297
LOC_53 1.278948
LOC_54 1.060266
LOC_55 1.077488
TOA_15 1.405927
TOA_20 1.028658
TOA_30 1.249601
TOA_32 1.005946
TOA_35 1.078285
TOA_38 1.177160
TOA_40 1.063196
TOA_42 1.010740
TOA_44 1.004523
TOA_45 1.000001
PPGROUP_11 1.158838
Removed AFTER this step: TOA_45
Call:
glm(formula = fla, family = "binomial", data = OPMAnalysisDataNoFamBinary)
Deviance Residuals:
Min 1Q Median 3Q Max
-2.8259 -0.7389 -0.1369 0.8190 3.2351
Coefficients:
Estimate Std. Error z value Pr(>|z|)
(Intercept) 2.165e+00 3.501e-01 6.183 6.28e-10 ***
GSEGRD -6.310e-02 1.543e-02 -4.088 4.35e-05 ***
SalaryOverUnderIndAvg 9.006e-07 2.407e-06 0.374 0.708324
LowerLimitAge -4.492e-02 2.664e-03 -16.860 < 2e-16 ***
BLS_FEDERAL_OtherSep_Rate 3.241e+00 3.376e-01 9.600 < 2e-16 ***
BLS_FEDERAL_Quits_Rate 4.753e-01 3.158e-01 1.505 0.132305
BLS_FEDERAL_JobOpenings_Level 1.707e-02 1.931e-03 8.842 < 2e-16 ***
BLS_FEDERAL_Layoffs_Rate 4.242e-02 1.230e-01 0.345 0.730206
LOSSqrt -7.826e-01 2.293e-02 -34.129 < 2e-16 ***
SEPCount_EFDATE_OCCLog -3.101e-02 1.612e-02 -1.924 0.054362 .
AGELVL_B1 -5.865e-01 2.200e-01 -2.666 0.007678 **
AGELVL_C1 -1.678e-01 8.533e-02 -1.966 0.049254 *
AGELVL_E1 2.741e-01 6.732e-02 4.071 4.68e-05 ***
AGELVL_F1 4.894e-01 7.015e-02 6.976 3.03e-12 ***
AGELVL_G1 7.122e-01 7.401e-02 9.623 < 2e-16 ***
AGELVL_H1 8.467e-01 7.623e-02 11.107 < 2e-16 ***
AGELVL_I1 7.176e-01 8.111e-02 8.847 < 2e-16 ***
LOC_011 -2.086e-01 1.746e-01 -1.195 0.232083
LOC_021 1.555e-01 2.560e-01 0.608 0.543406
LOC_041 5.282e-01 1.471e-01 3.590 0.000331 ***
LOC_051 8.277e-02 2.384e-01 0.347 0.728420
LOC_061 4.115e-01 1.007e-01 4.088 4.35e-05 ***
LOC_081 1.449e-01 1.347e-01 1.076 0.281892
LOC_091 -5.656e-02 3.326e-01 -0.170 0.864989
LOC_101 1.977e-01 5.833e-01 0.339 0.734644
LOC_121 3.582e-02 1.291e-01 0.277 0.781519
LOC_131 1.706e-01 1.257e-01 1.358 0.174603
LOC_151 -6.775e-02 1.679e-01 -0.403 0.686601
LOC_161 5.356e-02 2.754e-01 0.194 0.845806
LOC_171 -2.207e-01 1.517e-01 -1.455 0.145722
LOC_181 -2.268e-01 2.553e-01 -0.888 0.374317
LOC_191 2.165e-01 3.232e-01 0.670 0.502848
LOC_201 6.146e-01 2.607e-01 2.357 0.018410 *
LOC_211 -3.030e-02 2.090e-01 -0.145 0.884738
LOC_221 -1.106e-01 2.132e-01 -0.519 0.603787
LOC_231 7.004e-02 3.403e-01 0.206 0.836914
LOC_241 -1.085e-01 9.421e-02 -1.152 0.249486
LOC_251 1.446e-01 1.869e-01 0.774 0.438976
LOC_261 -7.089e-02 2.065e-01 -0.343 0.731407
LOC_271 2.891e-01 2.424e-01 1.192 0.233090
LOC_281 -1.241e-01 2.506e-01 -0.495 0.620487
LOC_291 -3.493e-01 1.763e-01 -1.982 0.047514 *
LOC_301 6.813e-01 2.453e-01 2.778 0.005476 **
LOC_311 1.336e-01 3.381e-01 0.395 0.692692
LOC_321 2.325e-01 2.485e-01 0.935 0.349541
LOC_331 1.985e-01 5.206e-01 0.381 0.703042
LOC_341 -1.816e-01 2.003e-01 -0.906 0.364685
LOC_351 4.734e-01 1.597e-01 2.963 0.003043 **
LOC_361 -2.832e-02 1.483e-01 -0.191 0.848601
LOC_371 6.847e-02 1.600e-01 0.428 0.668753
LOC_381 4.895e-01 3.698e-01 1.324 0.185597
LOC_391 -2.900e-01 1.504e-01 -1.927 0.053932 .
LOC_401 1.787e-01 1.726e-01 1.036 0.300293
LOC_411 1.162e-01 2.027e-01 0.573 0.566526
LOC_421 -2.943e-01 1.508e-01 -1.952 0.050904 .
LOC_441 3.392e-01 4.936e-01 0.687 0.491901
LOC_451 5.953e-02 2.297e-01 0.259 0.795515
LOC_461 8.151e-01 2.590e-01 3.146 0.001653 **
LOC_471 -1.783e-01 2.153e-01 -0.828 0.407746
LOC_481 2.522e-01 1.052e-01 2.398 0.016500 *
LOC_491 -1.842e-01 2.140e-01 -0.861 0.389502
LOC_501 -4.084e-01 5.947e-01 -0.687 0.492256
LOC_511 -1.548e-01 9.175e-02 -1.688 0.091467 .
LOC_531 3.985e-01 1.373e-01 2.903 0.003694 **
LOC_541 -6.119e-02 2.704e-01 -0.226 0.820980
LOC_551 -3.205e-01 2.548e-01 -1.258 0.208523
TOA_151 6.828e-02 6.912e-02 0.988 0.323244
TOA_201 1.128e+00 1.928e-01 5.849 4.96e-09 ***
TOA_301 3.461e-01 8.928e-02 3.877 0.000106 ***
TOA_321 4.203e-01 7.540e-01 0.557 0.577220
TOA_351 -5.356e-01 2.867e-01 -1.868 0.061749 .
TOA_381 3.285e-01 6.731e-02 4.881 1.06e-06 ***
TOA_401 -1.060e-01 1.765e-01 -0.601 0.548033
TOA_421 1.140e+00 4.414e-01 2.583 0.009781 **
TOA_441 2.164e+00 1.073e+00 2.018 0.043636 *
PPGROUP_111 -4.097e-01 1.172e-01 -3.495 0.000475 ***
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
(Dispersion parameter for binomial family taken to be 1)
Null deviance: 20683 on 14919 degrees of freedom
Residual deviance: 14543 on 14844 degrees of freedom
AIC: 14695
Number of Fisher Scoring iterations: 5
VIF
GSEGRD 1.709771
SalaryOverUnderIndAvg 1.362036
LowerLimitAge 2.331493
BLS_FEDERAL_OtherSep_Rate 1.780582
BLS_FEDERAL_Quits_Rate 1.239390
BLS_FEDERAL_JobOpenings_Level 1.189508
BLS_FEDERAL_Layoffs_Rate 1.400708
LOSSqrt 1.863360
SEPCount_EFDATE_OCCLog 1.110277
AGELVL_B 1.168845
AGELVL_C 1.661983
AGELVL_E 1.407668
AGELVL_F 1.284155
AGELVL_G 1.271141
AGELVL_H 1.360831
AGELVL_I 1.516931
LOC_01 1.155373
LOC_02 1.077814
LOC_04 1.297941
LOC_05 1.086855
LOC_06 1.643160
LOC_08 1.281207
LOC_09 1.043546
LOC_10 1.015115
LOC_12 1.333230
LOC_13 1.347045
LOC_15 1.173237
LOC_16 1.070530
LOC_17 1.194398
LOC_18 1.073553
LOC_19 1.046963
LOC_20 1.074933
LOC_21 1.115026
LOC_22 1.105494
LOC_23 1.041765
LOC_24 1.659471
LOC_25 1.116529
LOC_26 1.113368
LOC_27 1.083215
LOC_28 1.076568
LOC_29 1.166510
LOC_30 1.092223
LOC_31 1.044672
LOC_32 1.080506
LOC_33 1.019281
LOC_34 1.108202
LOC_35 1.235907
LOC_36 1.212547
LOC_37 1.199355
LOC_38 1.038948
LOC_39 1.218269
LOC_40 1.167172
LOC_41 1.121634
LOC_42 1.204074
LOC_44 1.018958
LOC_45 1.093417
LOC_46 1.088753
LOC_47 1.104412
LOC_48 1.597803
LOC_49 1.103006
LOC_50 1.014627
LOC_51 1.785718
LOC_53 1.278745
LOC_54 1.060235
LOC_55 1.077441
TOA_15 1.405743
TOA_20 1.028644
TOA_30 1.249555
TOA_32 1.005946
TOA_35 1.078261
TOA_38 1.177114
TOA_40 1.063136
TOA_42 1.010736
TOA_44 1.004521
PPGROUP_11 1.158849
Removed AFTER this step: LOC_21
Call:
glm(formula = fla, family = "binomial", data = OPMAnalysisDataNoFamBinary)
Deviance Residuals:
Min 1Q Median 3Q Max
-2.8258 -0.7389 -0.1369 0.8188 3.2357
Coefficients:
Estimate Std. Error z value Pr(>|z|)
(Intercept) 2.159e+00 3.482e-01 6.201 5.61e-10 ***
GSEGRD -6.284e-02 1.533e-02 -4.098 4.16e-05 ***
SalaryOverUnderIndAvg 9.164e-07 2.405e-06 0.381 0.70321
LowerLimitAge -4.494e-02 2.661e-03 -16.889 < 2e-16 ***
BLS_FEDERAL_OtherSep_Rate 3.241e+00 3.376e-01 9.599 < 2e-16 ***
BLS_FEDERAL_Quits_Rate 4.759e-01 3.158e-01 1.507 0.13178
BLS_FEDERAL_JobOpenings_Level 1.707e-02 1.931e-03 8.841 < 2e-16 ***
BLS_FEDERAL_Layoffs_Rate 4.248e-02 1.230e-01 0.345 0.72985
LOSSqrt -7.826e-01 2.293e-02 -34.131 < 2e-16 ***
SEPCount_EFDATE_OCCLog -3.111e-02 1.610e-02 -1.932 0.05334 .
AGELVL_B1 -5.861e-01 2.200e-01 -2.664 0.00772 **
AGELVL_C1 -1.677e-01 8.533e-02 -1.966 0.04933 *
AGELVL_E1 2.740e-01 6.732e-02 4.070 4.70e-05 ***
AGELVL_F1 4.894e-01 7.015e-02 6.976 3.03e-12 ***
AGELVL_G1 7.121e-01 7.400e-02 9.622 < 2e-16 ***
AGELVL_H1 8.466e-01 7.623e-02 11.106 < 2e-16 ***
AGELVL_I1 7.177e-01 8.111e-02 8.849 < 2e-16 ***
LOC_011 -2.055e-01 1.732e-01 -1.186 0.23554
LOC_021 1.588e-01 2.550e-01 0.623 0.53336
LOC_041 5.316e-01 1.452e-01 3.662 0.00025 ***
LOC_051 8.602e-02 2.373e-01 0.363 0.71697
LOC_061 4.145e-01 9.845e-02 4.210 2.55e-05 ***
LOC_081 1.481e-01 1.329e-01 1.114 0.26515
LOC_091 -5.332e-02 3.319e-01 -0.161 0.87236
LOC_101 2.011e-01 5.828e-01 0.345 0.73006
LOC_121 3.902e-02 1.272e-01 0.307 0.75911
LOC_131 1.738e-01 1.237e-01 1.405 0.16012
LOC_151 -6.454e-02 1.665e-01 -0.388 0.69819
LOC_161 5.689e-02 2.744e-01 0.207 0.83578
LOC_171 -2.177e-01 1.503e-01 -1.449 0.14744
LOC_181 -2.236e-01 2.544e-01 -0.879 0.37930
LOC_191 2.198e-01 3.224e-01 0.682 0.49538
LOC_201 6.179e-01 2.597e-01 2.380 0.01733 *
LOC_221 -1.074e-01 2.120e-01 -0.507 0.61248
LOC_231 7.316e-02 3.396e-01 0.215 0.82941
LOC_241 -1.056e-01 9.214e-02 -1.147 0.25156
LOC_251 1.476e-01 1.857e-01 0.794 0.42698
LOC_261 -6.769e-02 2.053e-01 -0.330 0.74167
LOC_271 2.924e-01 2.413e-01 1.211 0.22574
LOC_281 -1.208e-01 2.496e-01 -0.484 0.62833
LOC_291 -3.460e-01 1.748e-01 -1.980 0.04775 *
LOC_301 6.848e-01 2.441e-01 2.805 0.00503 **
LOC_311 1.369e-01 3.374e-01 0.406 0.68483
LOC_321 2.357e-01 2.475e-01 0.953 0.34084
LOC_331 2.018e-01 5.201e-01 0.388 0.69807
LOC_341 -1.786e-01 1.993e-01 -0.896 0.37003
LOC_351 4.768e-01 1.580e-01 3.018 0.00254 **
LOC_361 -2.524e-02 1.468e-01 -0.172 0.86350
LOC_371 7.174e-02 1.584e-01 0.453 0.65066
LOC_381 4.929e-01 3.690e-01 1.336 0.18169
LOC_391 -2.868e-01 1.489e-01 -1.927 0.05402 .
LOC_401 1.820e-01 1.711e-01 1.064 0.28748
LOC_411 1.194e-01 2.014e-01 0.593 0.55347
LOC_421 -2.913e-01 1.493e-01 -1.951 0.05104 .
LOC_441 3.424e-01 4.931e-01 0.694 0.48753
LOC_451 6.287e-02 2.286e-01 0.275 0.78327
LOC_461 8.187e-01 2.578e-01 3.175 0.00150 **
LOC_471 -1.750e-01 2.141e-01 -0.817 0.41383
LOC_481 2.554e-01 1.028e-01 2.486 0.01293 *
LOC_491 -1.810e-01 2.129e-01 -0.850 0.39529
LOC_501 -4.051e-01 5.943e-01 -0.682 0.49548
LOC_511 -1.518e-01 8.935e-02 -1.699 0.08929 .
LOC_531 4.017e-01 1.355e-01 2.964 0.00304 **
LOC_541 -5.801e-02 2.695e-01 -0.215 0.82958
LOC_551 -3.173e-01 2.539e-01 -1.250 0.21138
TOA_151 6.818e-02 6.912e-02 0.986 0.32391
TOA_201 1.128e+00 1.928e-01 5.850 4.93e-09 ***
TOA_301 3.471e-01 8.905e-02 3.898 9.71e-05 ***
TOA_321 4.200e-01 7.540e-01 0.557 0.57752
TOA_351 -5.349e-01 2.867e-01 -1.866 0.06204 .
TOA_381 3.283e-01 6.729e-02 4.879 1.07e-06 ***
TOA_401 -1.052e-01 1.764e-01 -0.597 0.55079
TOA_421 1.141e+00 4.414e-01 2.585 0.00975 **
TOA_441 2.166e+00 1.073e+00 2.020 0.04339 *
PPGROUP_111 -4.091e-01 1.172e-01 -3.492 0.00048 ***
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
(Dispersion parameter for binomial family taken to be 1)
Null deviance: 20683 on 14919 degrees of freedom
Residual deviance: 14543 on 14845 degrees of freedom
AIC: 14693
Number of Fisher Scoring iterations: 5
VIF
GSEGRD 1.687961
SalaryOverUnderIndAvg 1.359393
LowerLimitAge 2.325664
BLS_FEDERAL_OtherSep_Rate 1.780297
BLS_FEDERAL_Quits_Rate 1.239183
BLS_FEDERAL_JobOpenings_Level 1.189397
BLS_FEDERAL_Layoffs_Rate 1.400682
LOSSqrt 1.863295
SEPCount_EFDATE_OCCLog 1.108137
AGELVL_B 1.168649
AGELVL_C 1.661953
AGELVL_E 1.407636
AGELVL_F 1.284158
AGELVL_G 1.270875
AGELVL_H 1.360774
AGELVL_I 1.516882
LOC_01 1.137639
LOC_02 1.069443
LOC_04 1.263589
LOC_05 1.077198
LOC_06 1.572206
LOC_08 1.247572
LOC_09 1.038844
LOC_10 1.013493
LOC_12 1.294234
LOC_13 1.305744
LOC_15 1.152869
LOC_16 1.063066
LOC_17 1.171818
LOC_18 1.065537
LOC_19 1.041875
LOC_20 1.066436
LOC_22 1.093275
LOC_23 1.037582
LOC_24 1.587481
LOC_25 1.103363
LOC_26 1.100598
LOC_27 1.073679
LOC_28 1.067816
LOC_29 1.147164
LOC_30 1.081931
LOC_31 1.039929
LOC_32 1.071660
LOC_33 1.017328
LOC_34 1.096765
LOC_35 1.208682
LOC_36 1.187650
LOC_37 1.175472
LOC_38 1.034809
LOC_39 1.192786
LOC_40 1.147430
LOC_41 1.108226
LOC_42 1.180730
LOC_44 1.017009
LOC_45 1.082438
LOC_46 1.078691
LOC_47 1.092249
LOC_48 1.525339
LOC_49 1.091052
LOC_50 1.013096
LOC_51 1.693946
LOC_53 1.246368
LOC_54 1.053254
LOC_55 1.069242
TOA_15 1.405635
TOA_20 1.028583
TOA_30 1.243128
TOA_32 1.005938
TOA_35 1.077989
TOA_38 1.176425
TOA_40 1.062152
TOA_42 1.010677
TOA_44 1.004296
PPGROUP_11 1.157491
Removed AFTER this step: LOC_09
Call:
glm(formula = fla, family = "binomial", data = OPMAnalysisDataNoFamBinary)
Deviance Residuals:
Min 1Q Median 3Q Max
-2.8257 -0.7391 -0.1369 0.8188 3.2362
Coefficients:
Estimate Std. Error z value Pr(>|z|)
(Intercept) 2.156e+00 3.477e-01 6.202 5.58e-10 ***
GSEGRD -6.271e-02 1.531e-02 -4.096 4.21e-05 ***
SalaryOverUnderIndAvg 9.067e-07 2.405e-06 0.377 0.706118
LowerLimitAge -4.495e-02 2.660e-03 -16.897 < 2e-16 ***
BLS_FEDERAL_OtherSep_Rate 3.240e+00 3.376e-01 9.599 < 2e-16 ***
BLS_FEDERAL_Quits_Rate 4.761e-01 3.158e-01 1.508 0.131633
BLS_FEDERAL_JobOpenings_Level 1.706e-02 1.931e-03 8.839 < 2e-16 ***
BLS_FEDERAL_Layoffs_Rate 4.251e-02 1.230e-01 0.346 0.729666
LOSSqrt -7.826e-01 2.293e-02 -34.132 < 2e-16 ***
SEPCount_EFDATE_OCCLog -3.111e-02 1.610e-02 -1.932 0.053346 .
AGELVL_B1 -5.856e-01 2.200e-01 -2.662 0.007764 **
AGELVL_C1 -1.677e-01 8.533e-02 -1.965 0.049429 *
AGELVL_E1 2.742e-01 6.732e-02 4.073 4.65e-05 ***
AGELVL_F1 4.895e-01 7.015e-02 6.977 3.01e-12 ***
AGELVL_G1 7.123e-01 7.399e-02 9.626 < 2e-16 ***
AGELVL_H1 8.468e-01 7.623e-02 11.108 < 2e-16 ***
AGELVL_I1 7.176e-01 8.111e-02 8.848 < 2e-16 ***
LOC_011 -2.036e-01 1.728e-01 -1.178 0.238719
LOC_021 1.609e-01 2.546e-01 0.632 0.527466
LOC_041 5.336e-01 1.446e-01 3.690 0.000225 ***
LOC_051 8.797e-02 2.370e-01 0.371 0.710477
LOC_061 4.165e-01 9.768e-02 4.264 2.01e-05 ***
LOC_081 1.500e-01 1.324e-01 1.133 0.257194
LOC_101 2.032e-01 5.827e-01 0.349 0.727241
LOC_121 4.090e-02 1.267e-01 0.323 0.746827
LOC_131 1.756e-01 1.232e-01 1.426 0.153930
LOC_151 -6.258e-02 1.660e-01 -0.377 0.706187
LOC_161 5.888e-02 2.742e-01 0.215 0.829957
LOC_171 -2.158e-01 1.498e-01 -1.441 0.149706
LOC_181 -2.216e-01 2.541e-01 -0.872 0.382975
LOC_191 2.218e-01 3.221e-01 0.688 0.491157
LOC_201 6.199e-01 2.594e-01 2.390 0.016850 *
LOC_221 -1.054e-01 2.117e-01 -0.498 0.618403
LOC_231 7.513e-02 3.394e-01 0.221 0.824795
LOC_241 -1.039e-01 9.149e-02 -1.136 0.256143
LOC_251 1.494e-01 1.854e-01 0.806 0.420288
LOC_261 -6.571e-02 2.050e-01 -0.321 0.748543
LOC_271 2.944e-01 2.410e-01 1.222 0.221896
LOC_281 -1.189e-01 2.493e-01 -0.477 0.633497
LOC_291 -3.440e-01 1.744e-01 -1.973 0.048474 *
LOC_301 6.869e-01 2.438e-01 2.818 0.004838 **
LOC_311 1.389e-01 3.372e-01 0.412 0.680409
LOC_321 2.377e-01 2.471e-01 0.962 0.336065
LOC_331 2.038e-01 5.199e-01 0.392 0.695063
LOC_341 -1.768e-01 1.989e-01 -0.889 0.374233
LOC_351 4.788e-01 1.575e-01 3.040 0.002367 **
LOC_361 -2.333e-02 1.463e-01 -0.159 0.873316
LOC_371 7.368e-02 1.580e-01 0.466 0.640904
LOC_381 4.949e-01 3.688e-01 1.342 0.179617
LOC_391 -2.849e-01 1.484e-01 -1.920 0.054855 .
LOC_401 1.839e-01 1.707e-01 1.077 0.281257
LOC_411 1.213e-01 2.011e-01 0.603 0.546185
LOC_421 -2.895e-01 1.489e-01 -1.945 0.051828 .
LOC_441 3.444e-01 4.930e-01 0.699 0.484861
LOC_451 6.485e-02 2.282e-01 0.284 0.776292
LOC_461 8.208e-01 2.575e-01 3.187 0.001436 **
LOC_471 -1.730e-01 2.138e-01 -0.809 0.418320
LOC_481 2.574e-01 1.021e-01 2.522 0.011663 *
LOC_491 -1.790e-01 2.125e-01 -0.842 0.399616
LOC_501 -4.031e-01 5.941e-01 -0.678 0.497491
LOC_511 -1.500e-01 8.861e-02 -1.693 0.090533 .
LOC_531 4.036e-01 1.350e-01 2.990 0.002788 **
LOC_541 -5.610e-02 2.693e-01 -0.208 0.834962
LOC_551 -3.153e-01 2.536e-01 -1.243 0.213726
TOA_151 6.805e-02 6.911e-02 0.985 0.324764
TOA_201 1.128e+00 1.928e-01 5.851 4.88e-09 ***
TOA_301 3.475e-01 8.900e-02 3.905 9.43e-05 ***
TOA_321 4.197e-01 7.541e-01 0.557 0.577788
TOA_351 -5.343e-01 2.867e-01 -1.864 0.062320 .
TOA_381 3.280e-01 6.727e-02 4.876 1.08e-06 ***
TOA_401 -1.051e-01 1.764e-01 -0.596 0.551374
TOA_421 1.141e+00 4.414e-01 2.585 0.009726 **
TOA_441 2.168e+00 1.072e+00 2.021 0.043244 *
PPGROUP_111 -4.090e-01 1.172e-01 -3.491 0.000481 ***
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
(Dispersion parameter for binomial family taken to be 1)
Null deviance: 20683 on 14919 degrees of freedom
Residual deviance: 14543 on 14846 degrees of freedom
AIC: 14691
Number of Fisher Scoring iterations: 5
VIF
GSEGRD 1.682643
SalaryOverUnderIndAvg 1.358526
LowerLimitAge 2.324361
BLS_FEDERAL_OtherSep_Rate 1.780344
BLS_FEDERAL_Quits_Rate 1.239175
BLS_FEDERAL_JobOpenings_Level 1.189204
BLS_FEDERAL_Layoffs_Rate 1.400701
LOSSqrt 1.863038
SEPCount_EFDATE_OCCLog 1.108135
AGELVL_B 1.168388
AGELVL_C 1.661927
AGELVL_E 1.407413
AGELVL_F 1.284126
AGELVL_G 1.270561
AGELVL_H 1.360678
AGELVL_I 1.516775
LOC_01 1.132667
LOC_02 1.066681
LOC_04 1.254313
LOC_05 1.074380
LOC_06 1.547704
LOC_08 1.237732
LOC_10 1.012969
LOC_12 1.283232
LOC_13 1.294435
LOC_15 1.146631
LOC_16 1.060904
LOC_17 1.164865
LOC_18 1.063023
LOC_19 1.040327
LOC_20 1.064050
LOC_22 1.089639
LOC_23 1.036233
LOC_24 1.565215
LOC_25 1.099081
LOC_26 1.096610
LOC_27 1.070737
LOC_28 1.065270
LOC_29 1.141599
LOC_30 1.078836
LOC_31 1.038588
LOC_32 1.068864
LOC_33 1.016711
LOC_34 1.093039
LOC_35 1.201465
LOC_36 1.179866
LOC_37 1.168641
LOC_38 1.033569
LOC_39 1.185309
LOC_40 1.141838
LOC_41 1.104098
LOC_42 1.173979
LOC_44 1.016359
LOC_45 1.079273
LOC_46 1.075906
LOC_47 1.088696
LOC_48 1.504064
LOC_49 1.087529
LOC_50 1.012661
LOC_51 1.665897
LOC_53 1.236436
LOC_54 1.051193
LOC_55 1.066639
TOA_15 1.405445
TOA_20 1.028496
TOA_30 1.241826
TOA_32 1.005933
TOA_35 1.077785
TOA_38 1.175781
TOA_40 1.062126
TOA_42 1.010652
TOA_44 1.004229
PPGROUP_11 1.157450
Removed AFTER this step: LOC_36
Call:
glm(formula = fla, family = "binomial", data = OPMAnalysisDataNoFamBinary)
Deviance Residuals:
Min 1Q Median 3Q Max
-2.8256 -0.7391 -0.1369 0.8186 3.2371
Coefficients:
Estimate Std. Error z value Pr(>|z|)
(Intercept) 2.149e+00 3.449e-01 6.231 4.62e-10 ***
GSEGRD -6.241e-02 1.519e-02 -4.107 4.01e-05 ***
SalaryOverUnderIndAvg 9.050e-07 2.405e-06 0.376 0.706622
LowerLimitAge -4.496e-02 2.659e-03 -16.905 < 2e-16 ***
BLS_FEDERAL_OtherSep_Rate 3.240e+00 3.376e-01 9.598 < 2e-16 ***
BLS_FEDERAL_Quits_Rate 4.761e-01 3.158e-01 1.508 0.131609
BLS_FEDERAL_JobOpenings_Level 1.707e-02 1.930e-03 8.841 < 2e-16 ***
BLS_FEDERAL_Layoffs_Rate 4.265e-02 1.230e-01 0.347 0.728828
LOSSqrt -7.827e-01 2.292e-02 -34.146 < 2e-16 ***
SEPCount_EFDATE_OCCLog -3.116e-02 1.610e-02 -1.936 0.052864 .
AGELVL_B1 -5.853e-01 2.200e-01 -2.661 0.007790 **
AGELVL_C1 -1.677e-01 8.533e-02 -1.965 0.049440 *
AGELVL_E1 2.740e-01 6.731e-02 4.071 4.69e-05 ***
AGELVL_F1 4.893e-01 7.015e-02 6.976 3.05e-12 ***
AGELVL_G1 7.121e-01 7.399e-02 9.625 < 2e-16 ***
AGELVL_H1 8.467e-01 7.623e-02 11.108 < 2e-16 ***
AGELVL_I1 7.178e-01 8.110e-02 8.850 < 2e-16 ***
LOC_011 -1.997e-01 1.711e-01 -1.167 0.243039
LOC_021 1.651e-01 2.533e-01 0.652 0.514460
LOC_041 5.378e-01 1.423e-01 3.780 0.000157 ***
LOC_051 9.208e-02 2.356e-01 0.391 0.695898
LOC_061 4.205e-01 9.443e-02 4.453 8.47e-06 ***
LOC_081 1.539e-01 1.300e-01 1.184 0.236349
LOC_101 2.075e-01 5.821e-01 0.356 0.721521
LOC_121 4.488e-02 1.242e-01 0.361 0.717851
LOC_131 1.795e-01 1.207e-01 1.487 0.136913
LOC_151 -5.857e-02 1.641e-01 -0.357 0.721125
LOC_161 6.303e-02 2.729e-01 0.231 0.817353
LOC_171 -2.119e-01 1.478e-01 -1.434 0.151637
LOC_181 -2.176e-01 2.528e-01 -0.861 0.389363
LOC_191 2.259e-01 3.211e-01 0.703 0.481749
LOC_201 6.240e-01 2.581e-01 2.418 0.015619 *
LOC_221 -1.014e-01 2.101e-01 -0.482 0.629481
LOC_231 7.918e-02 3.384e-01 0.234 0.814997
LOC_241 -1.003e-01 8.862e-02 -1.131 0.257852
LOC_251 1.533e-01 1.838e-01 0.834 0.404307
LOC_261 -6.157e-02 2.033e-01 -0.303 0.762028
LOC_271 2.986e-01 2.396e-01 1.247 0.212559
LOC_281 -1.148e-01 2.480e-01 -0.463 0.643378
LOC_291 -3.400e-01 1.725e-01 -1.971 0.048702 *
LOC_301 6.911e-01 2.423e-01 2.852 0.004340 **
LOC_311 1.430e-01 3.362e-01 0.425 0.670499
LOC_321 2.419e-01 2.458e-01 0.984 0.325121
LOC_331 2.081e-01 5.193e-01 0.401 0.688614
LOC_341 -1.729e-01 1.974e-01 -0.876 0.381219
LOC_351 4.829e-01 1.553e-01 3.109 0.001878 **
LOC_371 7.775e-02 1.559e-01 0.499 0.617944
LOC_381 4.991e-01 3.678e-01 1.357 0.174791
LOC_391 -2.809e-01 1.463e-01 -1.921 0.054767 .
LOC_401 1.879e-01 1.688e-01 1.113 0.265539
LOC_411 1.254e-01 1.994e-01 0.629 0.529422
LOC_421 -2.856e-01 1.468e-01 -1.945 0.051802 .
LOC_441 3.484e-01 4.924e-01 0.708 0.479219
LOC_451 6.898e-02 2.267e-01 0.304 0.760963
LOC_461 8.252e-01 2.560e-01 3.223 0.001269 **
LOC_471 -1.690e-01 2.123e-01 -0.796 0.426036
LOC_481 2.614e-01 9.888e-02 2.644 0.008201 **
LOC_491 -1.750e-01 2.110e-01 -0.829 0.406951
LOC_501 -3.991e-01 5.936e-01 -0.672 0.501407
LOC_511 -1.462e-01 8.531e-02 -1.713 0.086653 .
LOC_531 4.077e-01 1.326e-01 3.075 0.002108 **
LOC_541 -5.214e-02 2.681e-01 -0.194 0.845804
LOC_551 -3.111e-01 2.522e-01 -1.234 0.217366
TOA_151 6.799e-02 6.911e-02 0.984 0.325235
TOA_201 1.128e+00 1.928e-01 5.852 4.85e-09 ***
TOA_301 3.481e-01 8.894e-02 3.913 9.11e-05 ***
TOA_321 4.194e-01 7.541e-01 0.556 0.578107
TOA_351 -5.338e-01 2.866e-01 -1.862 0.062570 .
TOA_381 3.276e-01 6.721e-02 4.874 1.09e-06 ***
TOA_401 -1.044e-01 1.763e-01 -0.592 0.553758
TOA_421 1.141e+00 4.414e-01 2.586 0.009714 **
TOA_441 2.171e+00 1.072e+00 2.024 0.042924 *
PPGROUP_111 -4.087e-01 1.171e-01 -3.488 0.000486 ***
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
(Dispersion parameter for binomial family taken to be 1)
Null deviance: 20683 on 14919 degrees of freedom
Residual deviance: 14544 on 14847 degrees of freedom
AIC: 14690
Number of Fisher Scoring iterations: 5
VIF
GSEGRD 1.657660
SalaryOverUnderIndAvg 1.358527
LowerLimitAge 2.323183
BLS_FEDERAL_OtherSep_Rate 1.780218
BLS_FEDERAL_Quits_Rate 1.239155
BLS_FEDERAL_JobOpenings_Level 1.189094
BLS_FEDERAL_Layoffs_Rate 1.400661
LOSSqrt 1.861913
SEPCount_EFDATE_OCCLog 1.107623
AGELVL_B 1.168345
AGELVL_C 1.661945
AGELVL_E 1.407111
AGELVL_F 1.283799
AGELVL_G 1.270373
AGELVL_H 1.360653
AGELVL_I 1.516606
LOC_01 1.109664
LOC_02 1.055243
LOC_04 1.213554
LOC_05 1.061690
LOC_06 1.446196
LOC_08 1.193965
LOC_10 1.010876
LOC_12 1.233387
LOC_13 1.243048
LOC_15 1.120307
LOC_16 1.051323
LOC_17 1.133941
LOC_18 1.052290
LOC_19 1.033660
LOC_20 1.053602
LOC_22 1.073896
LOC_23 1.030415
LOC_24 1.468350
LOC_25 1.080265
LOC_26 1.079018
LOC_27 1.057836
LOC_28 1.054198
LOC_29 1.116958
LOC_30 1.065860
LOC_31 1.032410
LOC_32 1.057221
LOC_33 1.014011
LOC_34 1.076617
LOC_35 1.168674
LOC_37 1.138096
LOC_38 1.028209
LOC_39 1.151499
LOC_40 1.116769
LOC_41 1.086205
LOC_42 1.142333
LOC_44 1.013695
LOC_45 1.065360
LOC_46 1.063668
LOC_47 1.073126
LOC_48 1.412089
LOC_49 1.072259
LOC_50 1.010826
LOC_51 1.544234
LOC_53 1.193008
LOC_54 1.042254
LOC_55 1.055436
TOA_15 1.405458
TOA_20 1.028455
TOA_30 1.240230
TOA_32 1.005925
TOA_35 1.077661
TOA_38 1.173642
TOA_40 1.061551
TOA_42 1.010642
TOA_44 1.003920
PPGROUP_11 1.156931
Removed AFTER this step: LOC_54
Call:
glm(formula = fla, family = "binomial", data = OPMAnalysisDataNoFamBinary)
Deviance Residuals:
Min 1Q Median 3Q Max
-2.8257 -0.7396 -0.1369 0.8188 3.2376
Coefficients:
Estimate Std. Error z value Pr(>|z|)
(Intercept) 2.147e+00 3.446e-01 6.229 4.70e-10 ***
GSEGRD -6.229e-02 1.518e-02 -4.103 4.09e-05 ***
SalaryOverUnderIndAvg 9.247e-07 2.402e-06 0.385 0.700315
LowerLimitAge -4.496e-02 2.659e-03 -16.911 < 2e-16 ***
BLS_FEDERAL_OtherSep_Rate 3.241e+00 3.375e-01 9.601 < 2e-16 ***
BLS_FEDERAL_Quits_Rate 4.753e-01 3.157e-01 1.505 0.132269
BLS_FEDERAL_JobOpenings_Level 1.707e-02 1.930e-03 8.841 < 2e-16 ***
BLS_FEDERAL_Layoffs_Rate 4.268e-02 1.230e-01 0.347 0.728648
LOSSqrt -7.828e-01 2.292e-02 -34.152 < 2e-16 ***
SEPCount_EFDATE_OCCLog -3.120e-02 1.610e-02 -1.938 0.052605 .
AGELVL_B1 -5.848e-01 2.199e-01 -2.659 0.007834 **
AGELVL_C1 -1.674e-01 8.532e-02 -1.962 0.049707 *
AGELVL_E1 2.741e-01 6.731e-02 4.072 4.66e-05 ***
AGELVL_F1 4.893e-01 7.015e-02 6.976 3.03e-12 ***
AGELVL_G1 7.122e-01 7.398e-02 9.626 < 2e-16 ***
AGELVL_H1 8.468e-01 7.623e-02 11.109 < 2e-16 ***
AGELVL_I1 7.177e-01 8.110e-02 8.850 < 2e-16 ***
LOC_011 -1.973e-01 1.706e-01 -1.157 0.247438
LOC_021 1.674e-01 2.530e-01 0.662 0.508226
LOC_041 5.403e-01 1.417e-01 3.813 0.000137 ***
LOC_051 9.451e-02 2.352e-01 0.402 0.687858
LOC_061 4.226e-01 9.377e-02 4.507 6.56e-06 ***
LOC_081 1.562e-01 1.295e-01 1.207 0.227592
LOC_101 2.100e-01 5.819e-01 0.361 0.718237
LOC_121 4.725e-02 1.236e-01 0.382 0.702268
LOC_131 1.819e-01 1.201e-01 1.514 0.130008
LOC_151 -5.631e-02 1.637e-01 -0.344 0.730806
LOC_161 6.548e-02 2.726e-01 0.240 0.810172
LOC_171 -2.097e-01 1.474e-01 -1.423 0.154724
LOC_181 -2.152e-01 2.525e-01 -0.852 0.394088
LOC_191 2.284e-01 3.209e-01 0.712 0.476621
LOC_201 6.265e-01 2.578e-01 2.430 0.015092 *
LOC_221 -9.900e-02 2.098e-01 -0.472 0.636978
LOC_231 8.146e-02 3.382e-01 0.241 0.809668
LOC_241 -9.819e-02 8.797e-02 -1.116 0.264344
LOC_251 1.554e-01 1.835e-01 0.847 0.397018
LOC_261 -5.924e-02 2.030e-01 -0.292 0.770395
LOC_271 3.010e-01 2.392e-01 1.258 0.208339
LOC_281 -1.124e-01 2.477e-01 -0.454 0.650014
LOC_291 -3.375e-01 1.720e-01 -1.962 0.049728 *
LOC_301 6.936e-01 2.420e-01 2.866 0.004156 **
LOC_311 1.455e-01 3.359e-01 0.433 0.664979
LOC_321 2.443e-01 2.455e-01 0.995 0.319647
LOC_331 2.106e-01 5.191e-01 0.406 0.684997
LOC_341 -1.707e-01 1.971e-01 -0.866 0.386370
LOC_351 4.854e-01 1.548e-01 3.135 0.001721 **
LOC_371 8.013e-02 1.554e-01 0.516 0.606119
LOC_381 5.016e-01 3.676e-01 1.364 0.172440
LOC_391 -2.786e-01 1.458e-01 -1.911 0.055976 .
LOC_401 1.903e-01 1.684e-01 1.130 0.258468
LOC_411 1.278e-01 1.991e-01 0.642 0.520836
LOC_421 -2.833e-01 1.464e-01 -1.935 0.052931 .
LOC_441 3.507e-01 4.923e-01 0.712 0.476221
LOC_451 7.136e-02 2.264e-01 0.315 0.752626
LOC_461 8.277e-01 2.557e-01 3.237 0.001209 **
LOC_471 -1.665e-01 2.119e-01 -0.786 0.431883
LOC_481 2.637e-01 9.816e-02 2.687 0.007212 **
LOC_491 -1.726e-01 2.107e-01 -0.819 0.412554
LOC_501 -3.965e-01 5.934e-01 -0.668 0.504015
LOC_511 -1.440e-01 8.457e-02 -1.703 0.088650 .
LOC_531 4.099e-01 1.321e-01 3.104 0.001911 **
LOC_551 -3.087e-01 2.519e-01 -1.225 0.220422
TOA_151 6.799e-02 6.911e-02 0.984 0.325227
TOA_201 1.129e+00 1.928e-01 5.855 4.78e-09 ***
TOA_301 3.489e-01 8.883e-02 3.928 8.55e-05 ***
TOA_321 4.193e-01 7.541e-01 0.556 0.578187
TOA_351 -5.332e-01 2.866e-01 -1.860 0.062829 .
TOA_381 3.272e-01 6.718e-02 4.870 1.11e-06 ***
TOA_401 -1.038e-01 1.763e-01 -0.589 0.556003
TOA_421 1.140e+00 4.414e-01 2.584 0.009771 **
TOA_441 2.173e+00 1.072e+00 2.026 0.042731 *
PPGROUP_111 -4.088e-01 1.171e-01 -3.490 0.000483 ***
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
(Dispersion parameter for binomial family taken to be 1)
Null deviance: 20683 on 14919 degrees of freedom
Residual deviance: 14544 on 14848 degrees of freedom
AIC: 14688
Number of Fisher Scoring iterations: 5
VIF
GSEGRD 1.654900
SalaryOverUnderIndAvg 1.356144
LowerLimitAge 2.322537
BLS_FEDERAL_OtherSep_Rate 1.779967
BLS_FEDERAL_Quits_Rate 1.238896
BLS_FEDERAL_JobOpenings_Level 1.189080
BLS_FEDERAL_Layoffs_Rate 1.400649
LOSSqrt 1.861550
SEPCount_EFDATE_OCCLog 1.107501
AGELVL_B 1.168203
AGELVL_C 1.661700
AGELVL_E 1.407073
AGELVL_F 1.283799
AGELVL_G 1.270386
AGELVL_H 1.360623
AGELVL_I 1.516608
LOC_01 1.104134
LOC_02 1.052987
LOC_04 1.203869
LOC_05 1.058690
LOC_06 1.426053
LOC_08 1.184262
LOC_10 1.010384
LOC_12 1.221491
LOC_13 1.230847
LOC_15 1.114678
LOC_16 1.049069
LOC_17 1.127233
LOC_18 1.049736
LOC_19 1.032040
LOC_20 1.051063
LOC_22 1.070241
LOC_23 1.029175
LOC_24 1.446867
LOC_25 1.076466
LOC_26 1.075257
LOC_27 1.055076
LOC_28 1.051507
LOC_29 1.111154
LOC_30 1.063039
LOC_31 1.030958
LOC_32 1.054476
LOC_33 1.013394
LOC_34 1.073303
LOC_35 1.161118
LOC_37 1.131070
LOC_38 1.027011
LOC_39 1.143769
LOC_40 1.111134
LOC_41 1.082089
LOC_42 1.135238
LOC_44 1.013110
LOC_45 1.062247
LOC_46 1.060953
LOC_47 1.069456
LOC_48 1.391448
LOC_49 1.068675
LOC_50 1.010342
LOC_51 1.517656
LOC_53 1.183730
LOC_55 1.052816
TOA_15 1.405450
TOA_20 1.028352
TOA_30 1.236945
TOA_32 1.005925
TOA_35 1.077547
TOA_38 1.172736
TOA_40 1.061219
TOA_42 1.010531
TOA_44 1.003834
PPGROUP_11 1.156872
Removed AFTER this step: LOC_16
Call:
glm(formula = fla, family = "binomial", data = OPMAnalysisDataNoFamBinary)
Deviance Residuals:
Min 1Q Median 3Q Max
-2.8259 -0.7395 -0.1369 0.8187 3.2369
Coefficients:
Estimate Std. Error z value Pr(>|z|)
(Intercept) 2.154e+00 3.434e-01 6.271 3.58e-10 ***
GSEGRD -6.265e-02 1.511e-02 -4.146 3.38e-05 ***
SalaryOverUnderIndAvg 8.918e-07 2.398e-06 0.372 0.710021
LowerLimitAge -4.495e-02 2.659e-03 -16.910 < 2e-16 ***
BLS_FEDERAL_OtherSep_Rate 3.239e+00 3.375e-01 9.599 < 2e-16 ***
BLS_FEDERAL_Quits_Rate 4.749e-01 3.157e-01 1.504 0.132570
BLS_FEDERAL_JobOpenings_Level 1.707e-02 1.930e-03 8.844 < 2e-16 ***
BLS_FEDERAL_Layoffs_Rate 4.307e-02 1.230e-01 0.350 0.726209
LOSSqrt -7.825e-01 2.289e-02 -34.183 < 2e-16 ***
SEPCount_EFDATE_OCCLog -3.127e-02 1.609e-02 -1.943 0.051975 .
AGELVL_B1 -5.852e-01 2.199e-01 -2.661 0.007790 **
AGELVL_C1 -1.674e-01 8.532e-02 -1.962 0.049730 *
AGELVL_E1 2.740e-01 6.731e-02 4.072 4.67e-05 ***
AGELVL_F1 4.896e-01 7.013e-02 6.982 2.92e-12 ***
AGELVL_G1 7.123e-01 7.399e-02 9.627 < 2e-16 ***
AGELVL_H1 8.470e-01 7.623e-02 11.111 < 2e-16 ***
AGELVL_I1 7.179e-01 8.110e-02 8.853 < 2e-16 ***
LOC_011 -2.003e-01 1.702e-01 -1.177 0.239318
LOC_021 1.644e-01 2.527e-01 0.651 0.515262
LOC_041 5.371e-01 1.411e-01 3.807 0.000141 ***
LOC_051 9.143e-02 2.349e-01 0.389 0.697093
LOC_061 4.200e-01 9.312e-02 4.510 6.48e-06 ***
LOC_081 1.534e-01 1.289e-01 1.190 0.234154
LOC_101 2.069e-01 5.817e-01 0.356 0.722062
LOC_121 4.430e-02 1.230e-01 0.360 0.718710
LOC_131 1.790e-01 1.195e-01 1.498 0.134259
LOC_151 -5.906e-02 1.633e-01 -0.362 0.717530
LOC_171 -2.124e-01 1.470e-01 -1.446 0.148290
LOC_181 -2.180e-01 2.522e-01 -0.865 0.387234
LOC_191 2.252e-01 3.206e-01 0.703 0.482337
LOC_201 6.233e-01 2.574e-01 2.421 0.015467 *
LOC_221 -1.020e-01 2.094e-01 -0.487 0.626127
LOC_231 7.852e-02 3.380e-01 0.232 0.816269
LOC_241 -1.006e-01 8.737e-02 -1.152 0.249362
LOC_251 1.528e-01 1.831e-01 0.834 0.404166
LOC_261 -6.211e-02 2.026e-01 -0.307 0.759177
LOC_271 2.980e-01 2.389e-01 1.247 0.212309
LOC_281 -1.155e-01 2.473e-01 -0.467 0.640603
LOC_291 -3.406e-01 1.715e-01 -1.986 0.047075 *
LOC_301 6.903e-01 2.416e-01 2.857 0.004273 **
LOC_311 1.423e-01 3.356e-01 0.424 0.671648
LOC_321 2.413e-01 2.452e-01 0.984 0.324962
LOC_331 2.076e-01 5.189e-01 0.400 0.689157
LOC_341 -1.734e-01 1.968e-01 -0.881 0.378371
LOC_351 4.822e-01 1.543e-01 3.126 0.001775 **
LOC_371 7.726e-02 1.549e-01 0.499 0.618043
LOC_381 4.983e-01 3.673e-01 1.357 0.174921
LOC_391 -2.814e-01 1.453e-01 -1.937 0.052698 .
LOC_401 1.873e-01 1.679e-01 1.115 0.264686
LOC_411 1.247e-01 1.986e-01 0.628 0.530157
LOC_421 -2.860e-01 1.459e-01 -1.960 0.050015 .
LOC_441 3.478e-01 4.921e-01 0.707 0.479689
LOC_451 6.833e-02 2.261e-01 0.302 0.762458
LOC_461 8.244e-01 2.553e-01 3.229 0.001244 **
LOC_471 -1.695e-01 2.115e-01 -0.801 0.422874
LOC_481 2.609e-01 9.743e-02 2.678 0.007416 **
LOC_491 -1.756e-01 2.103e-01 -0.835 0.403642
LOC_501 -3.994e-01 5.933e-01 -0.673 0.500805
LOC_511 -1.467e-01 8.385e-02 -1.749 0.080279 .
LOC_531 4.070e-01 1.315e-01 3.095 0.001970 **
LOC_551 -3.117e-01 2.516e-01 -1.239 0.215432
TOA_151 6.772e-02 6.910e-02 0.980 0.327049
TOA_201 1.128e+00 1.928e-01 5.853 4.83e-09 ***
TOA_301 3.483e-01 8.878e-02 3.923 8.75e-05 ***
TOA_321 4.197e-01 7.541e-01 0.557 0.577802
TOA_351 -5.345e-01 2.866e-01 -1.865 0.062149 .
TOA_381 3.272e-01 6.718e-02 4.870 1.12e-06 ***
TOA_401 -1.043e-01 1.763e-01 -0.591 0.554262
TOA_421 1.140e+00 4.414e-01 2.582 0.009810 **
TOA_441 2.171e+00 1.072e+00 2.025 0.042908 *
PPGROUP_111 -4.095e-01 1.171e-01 -3.496 0.000471 ***
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
(Dispersion parameter for binomial family taken to be 1)
Null deviance: 20683 on 14919 degrees of freedom
Residual deviance: 14544 on 14849 degrees of freedom
AIC: 14686
Number of Fisher Scoring iterations: 5
VIF
GSEGRD 1.638746
SalaryOverUnderIndAvg 1.351672
LowerLimitAge 2.321901
BLS_FEDERAL_OtherSep_Rate 1.779528
BLS_FEDERAL_Quits_Rate 1.238885
BLS_FEDERAL_JobOpenings_Level 1.188952
BLS_FEDERAL_Layoffs_Rate 1.400393
LOSSqrt 1.856960
SEPCount_EFDATE_OCCLog 1.107038
AGELVL_B 1.168157
AGELVL_C 1.661675
AGELVL_E 1.407045
AGELVL_F 1.283355
AGELVL_G 1.270317
AGELVL_H 1.360518
AGELVL_I 1.516401
LOC_01 1.098556
LOC_02 1.050485
LOC_04 1.193535
LOC_05 1.055547
LOC_06 1.406677
LOC_08 1.174566
LOC_10 1.009912
LOC_12 1.209466
LOC_13 1.218387
LOC_15 1.109244
LOC_17 1.120707
LOC_18 1.047377
LOC_19 1.030321
LOC_20 1.048376
LOC_22 1.066426
LOC_23 1.027841
LOC_24 1.427402
LOC_25 1.072694
LOC_26 1.071532
LOC_27 1.052160
LOC_28 1.048686
LOC_29 1.105063
LOC_30 1.059740
LOC_31 1.029346
LOC_32 1.051781
LOC_33 1.012808
LOC_34 1.070013
LOC_35 1.152912
LOC_37 1.124396
LOC_38 1.025631
LOC_39 1.136152
LOC_40 1.105113
LOC_41 1.077513
LOC_42 1.128536
LOC_44 1.012508
LOC_45 1.058950
LOC_46 1.057952
LOC_47 1.065801
LOC_48 1.370902
LOC_49 1.064935
LOC_50 1.009927
LOC_51 1.491672
LOC_53 1.173919
LOC_55 1.050285
TOA_15 1.405102
TOA_20 1.028264
TOA_30 1.235620
TOA_32 1.005920
TOA_35 1.077184
TOA_38 1.172716
TOA_40 1.061095
TOA_42 1.010499
TOA_44 1.003777
PPGROUP_11 1.156240
Removed AFTER this step: LOC_23
Call:
glm(formula = fla, family = "binomial", data = OPMAnalysisDataNoFamBinary)
Deviance Residuals:
Min 1Q Median 3Q Max
-2.8260 -0.7397 -0.1369 0.8187 3.2365
Coefficients:
Estimate Std. Error z value Pr(>|z|)
(Intercept) 2.158e+00 3.428e-01 6.295 3.07e-10 ***
GSEGRD -6.280e-02 1.509e-02 -4.161 3.17e-05 ***
SalaryOverUnderIndAvg 8.910e-07 2.398e-06 0.372 0.710264
LowerLimitAge -4.495e-02 2.659e-03 -16.909 < 2e-16 ***
BLS_FEDERAL_OtherSep_Rate 3.239e+00 3.375e-01 9.597 < 2e-16 ***
BLS_FEDERAL_Quits_Rate 4.754e-01 3.157e-01 1.506 0.132148
BLS_FEDERAL_JobOpenings_Level 1.707e-02 1.930e-03 8.842 < 2e-16 ***
BLS_FEDERAL_Layoffs_Rate 4.286e-02 1.230e-01 0.348 0.727502
LOSSqrt -7.824e-01 2.289e-02 -34.184 < 2e-16 ***
SEPCount_EFDATE_OCCLog -3.132e-02 1.609e-02 -1.947 0.051586 .
AGELVL_B1 -5.830e-01 2.197e-01 -2.654 0.007966 **
AGELVL_C1 -1.673e-01 8.532e-02 -1.961 0.049880 *
AGELVL_E1 2.739e-01 6.731e-02 4.069 4.71e-05 ***
AGELVL_F1 4.896e-01 7.013e-02 6.980 2.94e-12 ***
AGELVL_G1 7.123e-01 7.398e-02 9.628 < 2e-16 ***
AGELVL_H1 8.470e-01 7.622e-02 11.112 < 2e-16 ***
AGELVL_I1 7.182e-01 8.109e-02 8.856 < 2e-16 ***
LOC_011 -2.023e-01 1.700e-01 -1.190 0.233961
LOC_021 1.623e-01 2.525e-01 0.643 0.520500
LOC_041 5.350e-01 1.408e-01 3.800 0.000145 ***
LOC_051 8.937e-02 2.347e-01 0.381 0.703395
LOC_061 4.180e-01 9.273e-02 4.508 6.55e-06 ***
LOC_081 1.514e-01 1.287e-01 1.177 0.239263
LOC_101 2.048e-01 5.817e-01 0.352 0.724711
LOC_121 4.229e-02 1.227e-01 0.345 0.730350
LOC_131 1.770e-01 1.192e-01 1.485 0.137625
LOC_151 -6.109e-02 1.630e-01 -0.375 0.707854
LOC_171 -2.144e-01 1.467e-01 -1.462 0.143862
LOC_181 -2.200e-01 2.520e-01 -0.873 0.382669
LOC_191 2.231e-01 3.204e-01 0.696 0.486294
LOC_201 6.212e-01 2.573e-01 2.415 0.015753 *
LOC_221 -1.041e-01 2.092e-01 -0.497 0.618910
LOC_241 -1.025e-01 8.701e-02 -1.178 0.238768
LOC_251 1.508e-01 1.830e-01 0.824 0.409700
LOC_261 -6.416e-02 2.024e-01 -0.317 0.751252
LOC_271 2.958e-01 2.387e-01 1.239 0.215273
LOC_281 -1.175e-01 2.472e-01 -0.476 0.634400
LOC_291 -3.427e-01 1.713e-01 -2.000 0.045464 *
LOC_301 6.881e-01 2.414e-01 2.850 0.004366 **
LOC_311 1.401e-01 3.355e-01 0.418 0.676204
LOC_321 2.393e-01 2.450e-01 0.977 0.328762
LOC_331 2.054e-01 5.188e-01 0.396 0.692259
LOC_341 -1.754e-01 1.966e-01 -0.892 0.372279
LOC_351 4.801e-01 1.540e-01 3.117 0.001825 **
LOC_371 7.524e-02 1.547e-01 0.486 0.626693
LOC_381 4.961e-01 3.672e-01 1.351 0.176688
LOC_391 -2.835e-01 1.450e-01 -1.955 0.050596 .
LOC_401 1.852e-01 1.677e-01 1.105 0.269263
LOC_411 1.226e-01 1.984e-01 0.618 0.536760
LOC_421 -2.880e-01 1.457e-01 -1.977 0.048035 *
LOC_441 3.457e-01 4.920e-01 0.703 0.482286
LOC_451 6.629e-02 2.259e-01 0.293 0.769168
LOC_461 8.223e-01 2.552e-01 3.222 0.001271 **
LOC_471 -1.716e-01 2.113e-01 -0.812 0.416850
LOC_481 2.589e-01 9.705e-02 2.667 0.007643 **
LOC_491 -1.777e-01 2.101e-01 -0.846 0.397728
LOC_501 -4.015e-01 5.932e-01 -0.677 0.498578
LOC_511 -1.486e-01 8.341e-02 -1.782 0.074736 .
LOC_531 4.049e-01 1.312e-01 3.086 0.002028 **
LOC_551 -3.137e-01 2.514e-01 -1.248 0.212070
TOA_151 6.773e-02 6.910e-02 0.980 0.327002
TOA_201 1.128e+00 1.928e-01 5.851 4.89e-09 ***
TOA_301 3.476e-01 8.873e-02 3.917 8.95e-05 ***
TOA_321 4.198e-01 7.540e-01 0.557 0.577705
TOA_351 -5.359e-01 2.865e-01 -1.870 0.061420 .
TOA_381 3.270e-01 6.718e-02 4.868 1.13e-06 ***
TOA_401 -1.048e-01 1.763e-01 -0.595 0.552046
TOA_421 1.139e+00 4.414e-01 2.581 0.009841 **
TOA_441 2.169e+00 1.072e+00 2.023 0.043047 *
PPGROUP_111 -4.096e-01 1.171e-01 -3.498 0.000469 ***
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
(Dispersion parameter for binomial family taken to be 1)
Null deviance: 20683 on 14919 degrees of freedom
Residual deviance: 14544 on 14850 degrees of freedom
AIC: 14684
Number of Fisher Scoring iterations: 5
VIF
GSEGRD 1.635493
SalaryOverUnderIndAvg 1.351637
LowerLimitAge 2.321890
BLS_FEDERAL_OtherSep_Rate 1.779447
BLS_FEDERAL_Quits_Rate 1.238832
BLS_FEDERAL_JobOpenings_Level 1.188706
BLS_FEDERAL_Layoffs_Rate 1.400361
LOSSqrt 1.856547
SEPCount_EFDATE_OCCLog 1.106852
AGELVL_B 1.166101
AGELVL_C 1.661686
AGELVL_E 1.406940
AGELVL_F 1.283334
AGELVL_G 1.270330
AGELVL_H 1.360533
AGELVL_I 1.516174
LOC_01 1.095654
LOC_02 1.049075
LOC_04 1.188659
LOC_05 1.054045
LOC_06 1.394802
LOC_08 1.169349
LOC_10 1.009673
LOC_12 1.203474
LOC_13 1.212179
LOC_15 1.106082
LOC_17 1.116947
LOC_18 1.046191
LOC_19 1.029487
LOC_20 1.047073
LOC_22 1.064552
LOC_24 1.415517
LOC_25 1.070446
LOC_26 1.069504
LOC_27 1.050601
LOC_28 1.047330
LOC_29 1.102109
LOC_30 1.058144
LOC_31 1.028569
LOC_32 1.050431
LOC_33 1.012465
LOC_34 1.067863
LOC_35 1.149027
LOC_37 1.120893
LOC_38 1.024947
LOC_39 1.132050
LOC_40 1.102110
LOC_41 1.075232
LOC_42 1.124592
LOC_44 1.012161
LOC_45 1.057364
LOC_46 1.056550
LOC_47 1.063935
LOC_48 1.360220
LOC_49 1.063052
LOC_50 1.009711
LOC_51 1.476127
LOC_53 1.168301
LOC_55 1.048946
TOA_15 1.405138
TOA_20 1.028186
TOA_30 1.234263
TOA_32 1.005920
TOA_35 1.076690
TOA_38 1.172635
TOA_40 1.060882
TOA_42 1.010477
TOA_44 1.003742
PPGROUP_11 1.156210
Removed AFTER this step: LOC_45
Call:
glm(formula = fla, family = "binomial", data = OPMAnalysisDataNoFamBinary)
Deviance Residuals:
Min 1Q Median 3Q Max
-2.8264 -0.7398 -0.1369 0.8186 3.2359
Coefficients:
Estimate Std. Error z value Pr(>|z|)
(Intercept) 2.167e+00 3.416e-01 6.344 2.24e-10 ***
GSEGRD -6.319e-02 1.504e-02 -4.203 2.63e-05 ***
SalaryOverUnderIndAvg 8.598e-07 2.396e-06 0.359 0.719708
LowerLimitAge -4.493e-02 2.657e-03 -16.909 < 2e-16 ***
BLS_FEDERAL_OtherSep_Rate 3.239e+00 3.375e-01 9.598 < 2e-16 ***
BLS_FEDERAL_Quits_Rate 4.745e-01 3.157e-01 1.503 0.132832
BLS_FEDERAL_JobOpenings_Level 1.706e-02 1.930e-03 8.840 < 2e-16 ***
BLS_FEDERAL_Layoffs_Rate 4.296e-02 1.230e-01 0.349 0.726862
LOSSqrt -7.824e-01 2.289e-02 -34.185 < 2e-16 ***
SEPCount_EFDATE_OCCLog -3.135e-02 1.609e-02 -1.948 0.051365 .
AGELVL_B1 -5.840e-01 2.197e-01 -2.658 0.007851 **
AGELVL_C1 -1.674e-01 8.532e-02 -1.962 0.049720 *
AGELVL_E1 2.739e-01 6.731e-02 4.069 4.73e-05 ***
AGELVL_F1 4.899e-01 7.012e-02 6.986 2.82e-12 ***
AGELVL_G1 7.129e-01 7.396e-02 9.639 < 2e-16 ***
AGELVL_H1 8.474e-01 7.621e-02 11.120 < 2e-16 ***
AGELVL_I1 7.184e-01 8.109e-02 8.860 < 2e-16 ***
LOC_011 -2.061e-01 1.695e-01 -1.216 0.223980
LOC_021 1.583e-01 2.522e-01 0.628 0.530074
LOC_041 5.308e-01 1.401e-01 3.790 0.000151 ***
LOC_051 8.535e-02 2.343e-01 0.364 0.715696
LOC_061 4.144e-01 9.193e-02 4.508 6.53e-06 ***
LOC_081 1.476e-01 1.280e-01 1.153 0.248811
LOC_101 2.008e-01 5.815e-01 0.345 0.729929
LOC_121 3.837e-02 1.220e-01 0.315 0.753075
LOC_131 1.731e-01 1.185e-01 1.461 0.143930
LOC_151 -6.485e-02 1.625e-01 -0.399 0.689882
LOC_171 -2.180e-01 1.462e-01 -1.492 0.135829
LOC_181 -2.239e-01 2.517e-01 -0.889 0.373782
LOC_191 2.190e-01 3.201e-01 0.684 0.493838
LOC_201 6.172e-01 2.569e-01 2.402 0.016298 *
LOC_221 -1.080e-01 2.088e-01 -0.517 0.604829
LOC_241 -1.058e-01 8.626e-02 -1.227 0.219857
LOC_251 1.473e-01 1.826e-01 0.807 0.419696
LOC_261 -6.805e-02 2.020e-01 -0.337 0.736181
LOC_271 2.918e-01 2.383e-01 1.224 0.220796
LOC_281 -1.216e-01 2.468e-01 -0.493 0.622356
LOC_291 -3.467e-01 1.708e-01 -2.030 0.042352 *
LOC_301 6.839e-01 2.410e-01 2.838 0.004540 **
LOC_311 1.360e-01 3.352e-01 0.406 0.685020
LOC_321 2.352e-01 2.446e-01 0.962 0.336282
LOC_331 2.013e-01 5.187e-01 0.388 0.697978
LOC_341 -1.789e-01 1.963e-01 -0.912 0.361961
LOC_351 4.760e-01 1.534e-01 3.103 0.001913 **
LOC_371 7.133e-02 1.541e-01 0.463 0.643497
LOC_381 4.919e-01 3.669e-01 1.340 0.180084
LOC_391 -2.873e-01 1.444e-01 -1.989 0.046703 *
LOC_401 1.813e-01 1.672e-01 1.085 0.277980
LOC_411 1.185e-01 1.979e-01 0.599 0.549253
LOC_421 -2.917e-01 1.452e-01 -2.009 0.044519 *
LOC_441 3.419e-01 4.918e-01 0.695 0.486913
LOC_461 8.179e-01 2.548e-01 3.211 0.001324 **
LOC_471 -1.756e-01 2.109e-01 -0.833 0.405121
LOC_481 2.550e-01 9.615e-02 2.652 0.008000 **
LOC_491 -1.816e-01 2.097e-01 -0.866 0.386433
LOC_501 -4.056e-01 5.931e-01 -0.684 0.494059
LOC_511 -1.522e-01 8.253e-02 -1.844 0.065154 .
LOC_531 4.011e-01 1.306e-01 3.072 0.002126 **
LOC_551 -3.177e-01 2.511e-01 -1.265 0.205694
TOA_151 6.730e-02 6.909e-02 0.974 0.329997
TOA_201 1.128e+00 1.928e-01 5.849 4.93e-09 ***
TOA_301 3.468e-01 8.868e-02 3.910 9.22e-05 ***
TOA_321 4.202e-01 7.540e-01 0.557 0.577355
TOA_351 -5.369e-01 2.865e-01 -1.874 0.060925 .
TOA_381 3.274e-01 6.717e-02 4.874 1.10e-06 ***
TOA_401 -1.055e-01 1.763e-01 -0.598 0.549608
TOA_421 1.139e+00 4.414e-01 2.581 0.009837 **
TOA_441 2.167e+00 1.072e+00 2.021 0.043296 *
PPGROUP_111 -4.104e-01 1.171e-01 -3.506 0.000455 ***
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
(Dispersion parameter for binomial family taken to be 1)
Null deviance: 20683 on 14919 degrees of freedom
Residual deviance: 14544 on 14851 degrees of freedom
AIC: 14682
Number of Fisher Scoring iterations: 5
VIF
GSEGRD 1.622738
SalaryOverUnderIndAvg 1.348899
LowerLimitAge 2.319650
BLS_FEDERAL_OtherSep_Rate 1.779386
BLS_FEDERAL_Quits_Rate 1.238715
BLS_FEDERAL_JobOpenings_Level 1.188649
BLS_FEDERAL_Layoffs_Rate 1.400307
LOSSqrt 1.856367
SEPCount_EFDATE_OCCLog 1.106791
AGELVL_B 1.165822
AGELVL_C 1.661625
AGELVL_E 1.406918
AGELVL_F 1.282980
AGELVL_G 1.269558
AGELVL_H 1.360004
AGELVL_I 1.515946
LOC_01 1.089328
LOC_02 1.046139
LOC_04 1.176431
LOC_05 1.050457
LOC_06 1.370739
LOC_08 1.157708
LOC_10 1.009095
LOC_12 1.189254
LOC_13 1.197700
LOC_15 1.099290
LOC_17 1.109069
LOC_18 1.043379
LOC_19 1.027585
LOC_20 1.044017
LOC_22 1.060106
LOC_24 1.391427
LOC_25 1.065889
LOC_26 1.064928
LOC_27 1.047198
LOC_28 1.044135
LOC_29 1.095148
LOC_30 1.054410
LOC_31 1.026741
LOC_32 1.047077
LOC_33 1.011737
LOC_34 1.063960
LOC_35 1.139519
LOC_37 1.112600
LOC_38 1.023370
LOC_39 1.123051
LOC_40 1.095179
LOC_41 1.070114
LOC_42 1.116521
LOC_44 1.011471
LOC_46 1.053036
LOC_47 1.059537
LOC_48 1.335241
LOC_49 1.058759
LOC_50 1.009143
LOC_51 1.445043
LOC_53 1.157121
LOC_55 1.045923
TOA_15 1.404428
TOA_20 1.028182
TOA_30 1.233026
TOA_32 1.005917
TOA_35 1.076566
TOA_38 1.172315
TOA_40 1.060716
TOA_42 1.010484
TOA_44 1.003670
PPGROUP_11 1.155575
Removed AFTER this step: LOC_12
Call:
glm(formula = fla, family = "binomial", data = OPMAnalysisDataNoFamBinary)
Deviance Residuals:
Min 1Q Median 3Q Max
-2.8263 -0.7402 -0.1370 0.8187 3.2342
Coefficients:
Estimate Std. Error z value Pr(>|z|)
(Intercept) 2.178e+00 3.397e-01 6.413 1.43e-10 ***
GSEGRD -6.365e-02 1.496e-02 -4.254 2.10e-05 ***
SalaryOverUnderIndAvg 7.841e-07 2.384e-06 0.329 0.742214
LowerLimitAge -4.489e-02 2.654e-03 -16.915 < 2e-16 ***
BLS_FEDERAL_OtherSep_Rate 3.237e+00 3.374e-01 9.594 < 2e-16 ***
BLS_FEDERAL_Quits_Rate 4.745e-01 3.157e-01 1.503 0.132905
BLS_FEDERAL_JobOpenings_Level 1.705e-02 1.929e-03 8.835 < 2e-16 ***
BLS_FEDERAL_Layoffs_Rate 4.351e-02 1.230e-01 0.354 0.723509
LOSSqrt -7.822e-01 2.287e-02 -34.203 < 2e-16 ***
SEPCount_EFDATE_OCCLog -3.136e-02 1.609e-02 -1.949 0.051304 .
AGELVL_B1 -5.842e-01 2.197e-01 -2.659 0.007836 **
AGELVL_C1 -1.671e-01 8.532e-02 -1.959 0.050124 .
AGELVL_E1 2.741e-01 6.730e-02 4.072 4.65e-05 ***
AGELVL_F1 4.900e-01 7.012e-02 6.988 2.78e-12 ***
AGELVL_G1 7.134e-01 7.393e-02 9.649 < 2e-16 ***
AGELVL_H1 8.479e-01 7.620e-02 11.127 < 2e-16 ***
AGELVL_I1 7.188e-01 8.108e-02 8.865 < 2e-16 ***
LOC_011 -2.132e-01 1.680e-01 -1.269 0.204416
LOC_021 1.515e-01 2.512e-01 0.603 0.546379
LOC_041 5.233e-01 1.380e-01 3.792 0.000150 ***
LOC_051 7.797e-02 2.331e-01 0.334 0.738069
LOC_061 4.081e-01 8.966e-02 4.551 5.33e-06 ***
LOC_081 1.408e-01 1.262e-01 1.116 0.264380
LOC_101 1.934e-01 5.811e-01 0.333 0.739279
LOC_131 1.661e-01 1.164e-01 1.428 0.153396
LOC_151 -7.155e-02 1.611e-01 -0.444 0.657011
LOC_171 -2.246e-01 1.447e-01 -1.553 0.120534
LOC_181 -2.310e-01 2.507e-01 -0.921 0.356844
LOC_191 2.116e-01 3.193e-01 0.663 0.507483
LOC_201 6.097e-01 2.558e-01 2.383 0.017151 *
LOC_221 -1.152e-01 2.075e-01 -0.555 0.578834
LOC_241 -1.120e-01 8.401e-02 -1.333 0.182469
LOC_251 1.410e-01 1.814e-01 0.777 0.437158
LOC_261 -7.502e-02 2.008e-01 -0.374 0.708646
LOC_271 2.848e-01 2.373e-01 1.200 0.230117
LOC_281 -1.289e-01 2.457e-01 -0.524 0.599934
LOC_291 -3.539e-01 1.692e-01 -2.092 0.036442 *
LOC_301 6.765e-01 2.398e-01 2.821 0.004787 **
LOC_311 1.285e-01 3.343e-01 0.384 0.700804
LOC_321 2.279e-01 2.435e-01 0.936 0.349273
LOC_331 1.938e-01 5.181e-01 0.374 0.708349
LOC_341 -1.853e-01 1.952e-01 -0.949 0.342520
LOC_351 4.686e-01 1.516e-01 3.092 0.001989 **
LOC_371 6.427e-02 1.525e-01 0.421 0.673391
LOC_381 4.845e-01 3.662e-01 1.323 0.185796
LOC_391 -2.943e-01 1.427e-01 -2.062 0.039225 *
LOC_401 1.743e-01 1.656e-01 1.052 0.292679
LOC_411 1.113e-01 1.966e-01 0.566 0.571235
LOC_421 -2.984e-01 1.436e-01 -2.078 0.037704 *
LOC_441 3.350e-01 4.912e-01 0.682 0.495282
LOC_461 8.104e-01 2.536e-01 3.195 0.001396 **
LOC_471 -1.828e-01 2.096e-01 -0.872 0.383283
LOC_481 2.481e-01 9.359e-02 2.651 0.008035 **
LOC_491 -1.888e-01 2.084e-01 -0.906 0.365092
LOC_501 -4.131e-01 5.926e-01 -0.697 0.485708
LOC_511 -1.587e-01 7.992e-02 -1.986 0.047086 *
LOC_531 3.943e-01 1.288e-01 3.062 0.002196 **
LOC_551 -3.249e-01 2.500e-01 -1.300 0.193720
TOA_151 6.706e-02 6.908e-02 0.971 0.331681
TOA_201 1.127e+00 1.928e-01 5.848 4.98e-09 ***
TOA_301 3.448e-01 8.846e-02 3.898 9.71e-05 ***
TOA_321 4.204e-01 7.540e-01 0.558 0.577123
TOA_351 -5.383e-01 2.864e-01 -1.879 0.060201 .
TOA_381 3.283e-01 6.710e-02 4.892 9.99e-07 ***
TOA_401 -1.068e-01 1.762e-01 -0.606 0.544492
TOA_421 1.138e+00 4.413e-01 2.579 0.009903 **
TOA_441 2.161e+00 1.072e+00 2.016 0.043792 *
PPGROUP_111 -4.106e-01 1.171e-01 -3.508 0.000452 ***
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
(Dispersion parameter for binomial family taken to be 1)
Null deviance: 20683 on 14919 degrees of freedom
Residual deviance: 14544 on 14852 degrees of freedom
AIC: 14680
Number of Fisher Scoring iterations: 5
VIF
GSEGRD 1.607245
SalaryOverUnderIndAvg 1.335121
LowerLimitAge 2.313769
BLS_FEDERAL_OtherSep_Rate 1.778650
BLS_FEDERAL_Quits_Rate 1.238726
BLS_FEDERAL_JobOpenings_Level 1.187870
BLS_FEDERAL_Layoffs_Rate 1.400016
LOSSqrt 1.853161
SEPCount_EFDATE_OCCLog 1.106784
AGELVL_B 1.165808
AGELVL_C 1.661360
AGELVL_E 1.406768
AGELVL_F 1.282978
AGELVL_G 1.268886
AGELVL_H 1.359556
AGELVL_I 1.515651
LOC_01 1.070131
LOC_02 1.038484
LOC_04 1.142519
LOC_05 1.039944
LOC_06 1.303912
LOC_08 1.124567
LOC_10 1.007463
LOC_13 1.155554
LOC_15 1.080483
LOC_17 1.086394
LOC_18 1.034998
LOC_19 1.021963
LOC_20 1.035220
LOC_22 1.047533
LOC_24 1.319782
LOC_25 1.052940
LOC_26 1.052145
LOC_27 1.037878
LOC_28 1.034888
LOC_29 1.075079
LOC_30 1.044380
LOC_31 1.021553
LOC_32 1.037710
LOC_33 1.009625
LOC_34 1.052621
LOC_35 1.112767
LOC_37 1.089040
LOC_38 1.019182
LOC_39 1.096588
LOC_40 1.075560
LOC_41 1.055726
LOC_42 1.092420
LOC_44 1.009451
LOC_46 1.043682
LOC_47 1.047064
LOC_48 1.265073
LOC_49 1.046265
LOC_50 1.007500
LOC_51 1.355118
LOC_53 1.125331
LOC_55 1.037232
TOA_15 1.404263
TOA_20 1.028151
TOA_30 1.226667
TOA_32 1.005916
TOA_35 1.076319
TOA_38 1.170155
TOA_40 1.060117
TOA_42 1.010434
TOA_44 1.003412
PPGROUP_11 1.155529
Removed AFTER this step: SalaryOverUnderIndAvg
Call:
glm(formula = fla, family = "binomial", data = OPMAnalysisDataNoFamBinary)
Deviance Residuals:
Min 1Q Median 3Q Max
-2.8239 -0.7404 -0.1371 0.8187 3.2336
Coefficients:
Estimate Std. Error z value Pr(>|z|)
(Intercept) 2.175776 0.339555 6.408 1.48e-10 ***
GSEGRD -0.064749 0.014587 -4.439 9.05e-06 ***
LowerLimitAge -0.044749 0.002619 -17.084 < 2e-16 ***
BLS_FEDERAL_OtherSep_Rate 3.237454 0.337409 9.595 < 2e-16 ***
BLS_FEDERAL_Quits_Rate 0.473729 0.315708 1.501 0.133478
BLS_FEDERAL_JobOpenings_Level 0.017049 0.001929 8.836 < 2e-16 ***
BLS_FEDERAL_Layoffs_Rate 0.043437 0.122983 0.353 0.723942
LOSSqrt -0.780266 0.022127 -35.263 < 2e-16 ***
SEPCount_EFDATE_OCCLog -0.031296 0.016088 -1.945 0.051740 .
AGELVL_B1 -0.582401 0.219620 -2.652 0.008005 **
AGELVL_C1 -0.166715 0.085303 -1.954 0.050655 .
AGELVL_E1 0.274455 0.067291 4.079 4.53e-05 ***
AGELVL_F1 0.490885 0.070067 7.006 2.45e-12 ***
AGELVL_G1 0.714289 0.073891 9.667 < 2e-16 ***
AGELVL_H1 0.848498 0.076183 11.138 < 2e-16 ***
AGELVL_I1 0.719356 0.081066 8.874 < 2e-16 ***
LOC_011 -0.216571 0.167642 -1.292 0.196404
LOC_021 0.154810 0.251050 0.617 0.537466
LOC_041 0.518842 0.137333 3.778 0.000158 ***
LOC_051 0.074755 0.232942 0.321 0.748273
LOC_061 0.412327 0.088709 4.648 3.35e-06 ***
LOC_081 0.140018 0.126144 1.110 0.267008
LOC_101 0.191489 0.581466 0.329 0.741913
LOC_131 0.163159 0.116029 1.406 0.159665
LOC_151 -0.070196 0.161051 -0.436 0.662937
LOC_171 -0.224413 0.144687 -1.551 0.120896
LOC_181 -0.233289 0.250535 -0.931 0.351769
LOC_191 0.208357 0.319128 0.653 0.513823
LOC_201 0.605408 0.255417 2.370 0.017775 *
LOC_221 -0.117771 0.207374 -0.568 0.570091
LOC_241 -0.110760 0.083929 -1.320 0.186936
LOC_251 0.142889 0.181389 0.788 0.430843
LOC_261 -0.075152 0.200762 -0.374 0.708156
LOC_271 0.284589 0.237319 1.199 0.230457
LOC_281 -0.132894 0.245377 -0.542 0.588100
LOC_291 -0.357066 0.168919 -2.114 0.034530 *
LOC_301 0.675073 0.239642 2.817 0.004847 **
LOC_311 0.124717 0.334130 0.373 0.708956
LOC_321 0.225988 0.243381 0.929 0.353131
LOC_331 0.192858 0.518035 0.372 0.709678
LOC_341 -0.182779 0.195126 -0.937 0.348902
LOC_351 0.464202 0.150974 3.075 0.002107 **
LOC_371 0.062336 0.152365 0.409 0.682450
LOC_381 0.482358 0.366138 1.317 0.187698
LOC_391 -0.295863 0.142628 -2.074 0.038045 *
LOC_401 0.171745 0.165451 1.038 0.299249
LOC_411 0.109486 0.196519 0.557 0.577441
LOC_421 -0.299964 0.143524 -2.090 0.036619 *
LOC_441 0.335371 0.491192 0.683 0.494753
LOC_461 0.807237 0.253384 3.186 0.001443 **
LOC_471 -0.185827 0.209412 -0.887 0.374877
LOC_481 0.247441 0.093566 2.645 0.008179 **
LOC_491 -0.191339 0.208289 -0.919 0.358293
LOC_501 -0.418294 0.592240 -0.706 0.480007
LOC_511 -0.158525 0.079918 -1.984 0.047300 *
LOC_531 0.394822 0.128757 3.066 0.002167 **
LOC_551 -0.326323 0.249927 -1.306 0.191664
TOA_151 0.066975 0.069074 0.970 0.332238
TOA_201 1.125992 0.192744 5.842 5.16e-09 ***
TOA_301 0.346066 0.088379 3.916 9.01e-05 ***
TOA_321 0.421249 0.754545 0.558 0.576652
TOA_351 -0.537868 0.286395 -1.878 0.060373 .
TOA_381 0.329542 0.066987 4.919 8.68e-07 ***
TOA_401 -0.103990 0.176010 -0.591 0.554643
TOA_421 1.139177 0.441345 2.581 0.009847 **
TOA_441 2.163222 1.072020 2.018 0.043602 *
PPGROUP_111 -0.407821 0.116710 -3.494 0.000475 ***
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
(Dispersion parameter for binomial family taken to be 1)
Null deviance: 20683 on 14919 degrees of freedom
Residual deviance: 14544 on 14853 degrees of freedom
AIC: 14678
Number of Fisher Scoring iterations: 5
VIF
GSEGRD 1.527142
LowerLimitAge 2.254026
BLS_FEDERAL_OtherSep_Rate 1.778706
BLS_FEDERAL_Quits_Rate 1.238709
BLS_FEDERAL_JobOpenings_Level 1.187876
BLS_FEDERAL_Layoffs_Rate 1.400012
LOSSqrt 1.735002
SEPCount_EFDATE_OCCLog 1.106562
AGELVL_B 1.165066
AGELVL_C 1.661044
AGELVL_E 1.406387
AGELVL_F 1.281202
AGELVL_G 1.267262
AGELVL_H 1.358699
AGELVL_I 1.514945
LOC_01 1.066097
LOC_02 1.036863
LOC_04 1.131292
LOC_05 1.038118
LOC_06 1.276608
LOC_08 1.124136
LOC_10 1.007351
LOC_13 1.148525
LOC_15 1.079801
LOC_17 1.086368
LOC_18 1.034179
LOC_19 1.020988
LOC_20 1.032491
LOC_22 1.046036
LOC_24 1.316978
LOC_25 1.051856
LOC_26 1.052143
LOC_27 1.037870
LOC_28 1.032325
LOC_29 1.071707
LOC_30 1.044110
LOC_31 1.020370
LOC_32 1.037130
LOC_33 1.009601
LOC_34 1.050963
LOC_35 1.103842
LOC_37 1.087433
LOC_38 1.018865
LOC_39 1.095332
LOC_40 1.073209
LOC_41 1.054876
LOC_42 1.091195
LOC_44 1.009445
LOC_46 1.042212
LOC_47 1.045038
LOC_48 1.264614
LOC_49 1.044829
LOC_50 1.006794
LOC_51 1.355129
LOC_53 1.125190
LOC_55 1.036940
TOA_15 1.404316
TOA_20 1.027572
TOA_30 1.224091
TOA_32 1.005899
TOA_35 1.076276
TOA_38 1.166152
TOA_40 1.057631
TOA_42 1.010401
TOA_44 1.003387
PPGROUP_11 1.149157
Removed AFTER this step: LOC_05
Call:
glm(formula = fla, family = "binomial", data = OPMAnalysisDataNoFamBinary)
Deviance Residuals:
Min 1Q Median 3Q Max
-2.8240 -0.7405 -0.1370 0.8188 3.2331
Coefficients:
Estimate Std. Error z value Pr(>|z|)
(Intercept) 2.181947 0.339009 6.436 1.22e-10 ***
GSEGRD -0.064984 0.014568 -4.461 8.17e-06 ***
LowerLimitAge -0.044744 0.002619 -17.083 < 2e-16 ***
BLS_FEDERAL_OtherSep_Rate 3.235696 0.337361 9.591 < 2e-16 ***
BLS_FEDERAL_Quits_Rate 0.474361 0.315701 1.503 0.132951
BLS_FEDERAL_JobOpenings_Level 0.017044 0.001929 8.834 < 2e-16 ***
BLS_FEDERAL_Layoffs_Rate 0.043687 0.122981 0.355 0.722413
LOSSqrt -0.780194 0.022125 -35.263 < 2e-16 ***
SEPCount_EFDATE_OCCLog -0.031332 0.016088 -1.948 0.051465 .
AGELVL_B1 -0.582689 0.219611 -2.653 0.007971 **
AGELVL_C1 -0.166374 0.085297 -1.951 0.051113 .
AGELVL_E1 0.275001 0.067270 4.088 4.35e-05 ***
AGELVL_F1 0.491499 0.070041 7.017 2.26e-12 ***
AGELVL_G1 0.714360 0.073892 9.668 < 2e-16 ***
AGELVL_H1 0.848880 0.076174 11.144 < 2e-16 ***
AGELVL_I1 0.719882 0.081047 8.882 < 2e-16 ***
LOC_011 -0.219559 0.167387 -1.312 0.189626
LOC_021 0.151665 0.250861 0.605 0.545460
LOC_041 0.515683 0.136979 3.765 0.000167 ***
LOC_061 0.409304 0.088209 4.640 3.48e-06 ***
LOC_081 0.137069 0.125812 1.089 0.275945
LOC_101 0.188118 0.581389 0.324 0.746267
LOC_131 0.160204 0.115664 1.385 0.166028
LOC_151 -0.073210 0.160777 -0.455 0.648857
LOC_171 -0.227338 0.144402 -1.574 0.115408
LOC_181 -0.236497 0.250339 -0.945 0.344808
LOC_191 0.205036 0.318969 0.643 0.520348
LOC_201 0.602291 0.255232 2.360 0.018286 *
LOC_221 -0.120900 0.207145 -0.584 0.559457
LOC_241 -0.113475 0.083503 -1.359 0.174169
LOC_251 0.139984 0.181163 0.773 0.439703
LOC_261 -0.078327 0.200520 -0.391 0.696079
LOC_271 0.281334 0.237109 1.187 0.235417
LOC_281 -0.136012 0.245184 -0.555 0.579076
LOC_291 -0.360232 0.168634 -2.136 0.032665 *
LOC_301 0.671848 0.239430 2.806 0.005016 **
LOC_311 0.121456 0.333977 0.364 0.716107
LOC_321 0.222746 0.243177 0.916 0.359676
LOC_331 0.189409 0.517923 0.366 0.714582
LOC_341 -0.185757 0.194909 -0.953 0.340568
LOC_351 0.461130 0.150672 3.060 0.002210 **
LOC_371 0.059257 0.152064 0.390 0.696769
LOC_381 0.479119 0.366004 1.309 0.190515
LOC_391 -0.298936 0.142308 -2.101 0.035674 *
LOC_401 0.168678 0.165173 1.021 0.307150
LOC_411 0.106294 0.196265 0.542 0.588106
LOC_421 -0.302888 0.143235 -2.115 0.034462 *
LOC_441 0.332335 0.491091 0.677 0.498578
LOC_461 0.803971 0.253183 3.175 0.001496 **
LOC_471 -0.188954 0.209187 -0.903 0.366378
LOC_481 0.244406 0.093088 2.626 0.008651 **
LOC_491 -0.194469 0.208063 -0.935 0.349963
LOC_501 -0.421394 0.592183 -0.712 0.476716
LOC_511 -0.161370 0.079428 -2.032 0.042188 *
LOC_531 0.391756 0.128402 3.051 0.002281 **
LOC_551 -0.329610 0.249722 -1.320 0.186867
TOA_151 0.066807 0.069071 0.967 0.333433
TOA_201 1.125603 0.192739 5.840 5.22e-09 ***
TOA_301 0.345155 0.088332 3.907 9.33e-05 ***
TOA_321 0.421617 0.754508 0.559 0.576300
TOA_351 -0.539106 0.286368 -1.883 0.059760 .
TOA_381 0.330298 0.066947 4.934 8.07e-07 ***
TOA_401 -0.104769 0.175998 -0.595 0.551652
TOA_421 1.138334 0.441339 2.579 0.009901 **
TOA_441 2.160817 1.071983 2.016 0.043829 *
PPGROUP_111 -0.408162 0.116707 -3.497 0.000470 ***
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
(Dispersion parameter for binomial family taken to be 1)
Null deviance: 20683 on 14919 degrees of freedom
Residual deviance: 14544 on 14854 degrees of freedom
AIC: 14676
Number of Fisher Scoring iterations: 5
VIF
GSEGRD 1.523291
LowerLimitAge 2.253889
BLS_FEDERAL_OtherSep_Rate 1.778212
BLS_FEDERAL_Quits_Rate 1.238671
BLS_FEDERAL_JobOpenings_Level 1.187809
BLS_FEDERAL_Layoffs_Rate 1.399937
LOSSqrt 1.734824
SEPCount_EFDATE_OCCLog 1.106515
AGELVL_B 1.165062
AGELVL_C 1.660760
AGELVL_E 1.405480
AGELVL_F 1.280258
AGELVL_G 1.267235
AGELVL_H 1.358388
AGELVL_I 1.514410
LOC_01 1.062813
LOC_02 1.035284
LOC_04 1.125493
LOC_06 1.262244
LOC_08 1.118187
LOC_10 1.007023
LOC_13 1.141310
LOC_15 1.076147
LOC_17 1.082074
LOC_18 1.032536
LOC_19 1.019915
LOC_20 1.031001
LOC_22 1.043732
LOC_24 1.303675
LOC_25 1.049244
LOC_26 1.049598
LOC_27 1.035979
LOC_28 1.030711
LOC_29 1.068064
LOC_30 1.042276
LOC_31 1.019429
LOC_32 1.035346
LOC_33 1.009167
LOC_34 1.048598
LOC_35 1.099412
LOC_37 1.083137
LOC_38 1.018094
LOC_39 1.090409
LOC_40 1.069642
LOC_41 1.052178
LOC_42 1.086817
LOC_44 1.009071
LOC_46 1.040532
LOC_47 1.042780
LOC_48 1.251728
LOC_49 1.042546
LOC_50 1.006526
LOC_51 1.338522
LOC_53 1.119020
LOC_55 1.035205
TOA_15 1.404253
TOA_20 1.027536
TOA_30 1.222811
TOA_32 1.005897
TOA_35 1.076091
TOA_38 1.164749
TOA_40 1.057430
TOA_42 1.010368
TOA_44 1.003338
PPGROUP_11 1.149065
Removed AFTER this step: LOC_10
Call:
glm(formula = fla, family = "binomial", data = OPMAnalysisDataNoFamBinary)
Deviance Residuals:
Min 1Q Median 3Q Max
-2.8240 -0.7404 -0.1370 0.8191 3.2328
Coefficients:
Estimate Std. Error z value Pr(>|z|)
(Intercept) 2.183520 0.338986 6.441 1.18e-10 ***
GSEGRD -0.065072 0.014566 -4.467 7.92e-06 ***
LowerLimitAge -0.044732 0.002619 -17.080 < 2e-16 ***
BLS_FEDERAL_OtherSep_Rate 3.235607 0.337361 9.591 < 2e-16 ***
BLS_FEDERAL_Quits_Rate 0.474348 0.315695 1.503 0.132955
BLS_FEDERAL_JobOpenings_Level 0.017046 0.001929 8.835 < 2e-16 ***
BLS_FEDERAL_Layoffs_Rate 0.043330 0.122976 0.352 0.724576
LOSSqrt -0.780185 0.022125 -35.262 < 2e-16 ***
SEPCount_EFDATE_OCCLog -0.031363 0.016088 -1.949 0.051240 .
AGELVL_B1 -0.583101 0.219612 -2.655 0.007927 **
AGELVL_C1 -0.166364 0.085298 -1.950 0.051129 .
AGELVL_E1 0.275066 0.067270 4.089 4.33e-05 ***
AGELVL_F1 0.491845 0.070032 7.023 2.17e-12 ***
AGELVL_G1 0.714311 0.073892 9.667 < 2e-16 ***
AGELVL_H1 0.848861 0.076173 11.144 < 2e-16 ***
AGELVL_I1 0.720007 0.081043 8.884 < 2e-16 ***
LOC_011 -0.220715 0.167351 -1.319 0.187212
LOC_021 0.150362 0.250835 0.599 0.548876
LOC_041 0.514379 0.136920 3.757 0.000172 ***
LOC_061 0.408107 0.088132 4.631 3.65e-06 ***
LOC_081 0.135873 0.125759 1.080 0.279954
LOC_131 0.159039 0.115609 1.376 0.168925
LOC_151 -0.074416 0.160735 -0.463 0.643383
LOC_171 -0.228463 0.144361 -1.583 0.113517
LOC_181 -0.237840 0.250308 -0.950 0.342016
LOC_191 0.203711 0.318947 0.639 0.523018
LOC_201 0.600993 0.255204 2.355 0.018525 *
LOC_221 -0.122169 0.207111 -0.590 0.555276
LOC_241 -0.114548 0.083438 -1.373 0.169798
LOC_251 0.138850 0.181130 0.767 0.443333
LOC_261 -0.079596 0.200483 -0.397 0.691351
LOC_271 0.280026 0.237079 1.181 0.237543
LOC_281 -0.137292 0.245156 -0.560 0.575467
LOC_291 -0.361475 0.168593 -2.144 0.032027 *
LOC_301 0.670537 0.239398 2.801 0.005096 **
LOC_311 0.120175 0.333956 0.360 0.718957
LOC_321 0.221361 0.243143 0.910 0.362604
LOC_331 0.188021 0.517907 0.363 0.716575
LOC_341 -0.186883 0.194879 -0.959 0.337576
LOC_351 0.459903 0.150625 3.053 0.002263 **
LOC_371 0.058013 0.152017 0.382 0.702741
LOC_381 0.477802 0.365988 1.306 0.191718
LOC_391 -0.300136 0.142262 -2.110 0.034880 *
LOC_401 0.167461 0.165131 1.014 0.310531
LOC_411 0.105014 0.196225 0.535 0.592532
LOC_421 -0.304008 0.143195 -2.123 0.033751 *
LOC_441 0.331101 0.491071 0.674 0.500158
LOC_461 0.802644 0.253153 3.171 0.001521 **
LOC_471 -0.190244 0.209152 -0.910 0.363034
LOC_481 0.243190 0.093013 2.615 0.008933 **
LOC_491 -0.195732 0.208031 -0.941 0.346767
LOC_501 -0.422795 0.592174 -0.714 0.475245
LOC_511 -0.162508 0.079352 -2.048 0.040566 *
LOC_531 0.390564 0.128349 3.043 0.002342 **
LOC_551 -0.330917 0.249692 -1.325 0.185072
TOA_151 0.067272 0.069058 0.974 0.329988
TOA_201 1.125437 0.192738 5.839 5.24e-09 ***
TOA_301 0.344844 0.088326 3.904 9.45e-05 ***
TOA_321 0.421811 0.754476 0.559 0.576109
TOA_351 -0.539355 0.286369 -1.883 0.059643 .
TOA_381 0.330795 0.066927 4.943 7.71e-07 ***
TOA_401 -0.104925 0.175998 -0.596 0.551059
TOA_421 1.138093 0.441338 2.579 0.009916 **
TOA_441 2.159903 1.071975 2.015 0.043917 *
PPGROUP_111 -0.407968 0.116706 -3.496 0.000473 ***
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
(Dispersion parameter for binomial family taken to be 1)
Null deviance: 20683 on 14919 degrees of freedom
Residual deviance: 14544 on 14855 degrees of freedom
AIC: 14674
Number of Fisher Scoring iterations: 5
VIF
GSEGRD 1.522785
LowerLimitAge 2.253353
BLS_FEDERAL_OtherSep_Rate 1.778210
BLS_FEDERAL_Quits_Rate 1.238664
BLS_FEDERAL_JobOpenings_Level 1.187809
BLS_FEDERAL_Layoffs_Rate 1.399811
LOSSqrt 1.734807
SEPCount_EFDATE_OCCLog 1.106482
AGELVL_B 1.165040
AGELVL_C 1.660770
AGELVL_E 1.405477
AGELVL_F 1.280008
AGELVL_G 1.267222
AGELVL_H 1.358360
AGELVL_I 1.514380
LOC_01 1.062331
LOC_02 1.035017
LOC_04 1.124519
LOC_06 1.260043
LOC_08 1.117227
LOC_13 1.140212
LOC_15 1.075572
LOC_17 1.081454
LOC_18 1.032254
LOC_19 1.019748
LOC_20 1.030746
LOC_22 1.043362
LOC_24 1.301641
LOC_25 1.048856
LOC_26 1.049201
LOC_27 1.035679
LOC_28 1.030445
LOC_29 1.067514
LOC_30 1.041979
LOC_31 1.019288
LOC_32 1.035027
LOC_33 1.009098
LOC_34 1.048271
LOC_35 1.098716
LOC_37 1.082450
LOC_38 1.017969
LOC_39 1.089676
LOC_40 1.069092
LOC_41 1.051757
LOC_42 1.086193
LOC_44 1.009011
LOC_46 1.040257
LOC_47 1.042404
LOC_48 1.249699
LOC_49 1.042181
LOC_50 1.006473
LOC_51 1.335913
LOC_53 1.118110
LOC_55 1.034938
TOA_15 1.403746
TOA_20 1.027529
TOA_30 1.222663
TOA_32 1.005897
TOA_35 1.076098
TOA_38 1.164156
TOA_40 1.057424
TOA_42 1.010366
TOA_44 1.003331
PPGROUP_11 1.149045
Removed AFTER this step: BLS_FEDERAL_Layoffs_Rate
Call:
glm(formula = fla, family = "binomial", data = OPMAnalysisDataNoFamBinary)
Deviance Residuals:
Min 1Q Median 3Q Max
-2.8215 -0.7397 -0.1370 0.8186 3.2323
Coefficients:
Estimate Std. Error z value Pr(>|z|)
(Intercept) 2.168303 0.336227 6.449 1.13e-10 ***
GSEGRD -0.065014 0.014565 -4.464 8.05e-06 ***
LowerLimitAge -0.044721 0.002619 -17.079 < 2e-16 ***
BLS_FEDERAL_OtherSep_Rate 3.294584 0.292911 11.248 < 2e-16 ***
BLS_FEDERAL_Quits_Rate 0.458451 0.312495 1.467 0.142358
BLS_FEDERAL_JobOpenings_Level 0.017241 0.001848 9.329 < 2e-16 ***
LOSSqrt -0.779860 0.022105 -35.280 < 2e-16 ***
SEPCount_EFDATE_OCCLog -0.031162 0.016078 -1.938 0.052599 .
AGELVL_B1 -0.583027 0.219636 -2.655 0.007942 **
AGELVL_C1 -0.166363 0.085301 -1.950 0.051139 .
AGELVL_E1 0.275089 0.067271 4.089 4.33e-05 ***
AGELVL_F1 0.491414 0.070019 7.018 2.25e-12 ***
AGELVL_G1 0.713908 0.073879 9.663 < 2e-16 ***
AGELVL_H1 0.848290 0.076152 11.139 < 2e-16 ***
AGELVL_I1 0.719584 0.081035 8.880 < 2e-16 ***
LOC_011 -0.221438 0.167386 -1.323 0.185864
LOC_021 0.149818 0.250785 0.597 0.550242
LOC_041 0.513942 0.136919 3.754 0.000174 ***
LOC_061 0.408131 0.088133 4.631 3.64e-06 ***
LOC_081 0.135394 0.125749 1.077 0.281616
LOC_131 0.158738 0.115600 1.373 0.169700
LOC_151 -0.074376 0.160743 -0.463 0.643577
LOC_171 -0.228818 0.144361 -1.585 0.112958
LOC_181 -0.238110 0.250331 -0.951 0.341512
LOC_191 0.204350 0.319001 0.641 0.521787
LOC_201 0.602332 0.255176 2.360 0.018252 *
LOC_221 -0.121545 0.207086 -0.587 0.557252
LOC_241 -0.115021 0.083428 -1.379 0.167991
LOC_251 0.139073 0.181104 0.768 0.442535
LOC_261 -0.079572 0.200498 -0.397 0.691462
LOC_271 0.279427 0.237010 1.179 0.238410
LOC_281 -0.136989 0.245126 -0.559 0.576264
LOC_291 -0.360644 0.168620 -2.139 0.032452 *
LOC_301 0.670778 0.239380 2.802 0.005076 **
LOC_311 0.120621 0.333958 0.361 0.717960
LOC_321 0.220598 0.243126 0.907 0.364226
LOC_331 0.187430 0.517922 0.362 0.717435
LOC_341 -0.185904 0.194873 -0.954 0.340096
LOC_351 0.459787 0.150637 3.052 0.002271 **
LOC_371 0.057762 0.152012 0.380 0.703957
LOC_381 0.477269 0.366038 1.304 0.192275
LOC_391 -0.299478 0.142245 -2.105 0.035259 *
LOC_401 0.167606 0.165098 1.015 0.310015
LOC_411 0.105391 0.196253 0.537 0.591258
LOC_421 -0.304551 0.143194 -2.127 0.033433 *
LOC_441 0.331390 0.491077 0.675 0.499788
LOC_461 0.802590 0.253180 3.170 0.001524 **
LOC_471 -0.191428 0.209120 -0.915 0.359983
LOC_481 0.243378 0.093008 2.617 0.008877 **
LOC_491 -0.195817 0.208006 -0.941 0.346499
LOC_501 -0.425532 0.591928 -0.719 0.472208
LOC_511 -0.162115 0.079342 -2.043 0.041028 *
LOC_531 0.390516 0.128340 3.043 0.002344 **
LOC_551 -0.330474 0.249731 -1.323 0.185730
TOA_151 0.067497 0.069057 0.977 0.328362
TOA_201 1.125809 0.192719 5.842 5.17e-09 ***
TOA_301 0.344702 0.088320 3.903 9.51e-05 ***
TOA_321 0.417433 0.754276 0.553 0.579975
TOA_351 -0.538782 0.286395 -1.881 0.059937 .
TOA_381 0.330875 0.066926 4.944 7.66e-07 ***
TOA_401 -0.105467 0.175986 -0.599 0.548978
TOA_421 1.139273 0.441399 2.581 0.009850 **
TOA_441 2.159821 1.071976 2.015 0.043925 *
PPGROUP_111 -0.407455 0.116689 -3.492 0.000480 ***
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
(Dispersion parameter for binomial family taken to be 1)
Null deviance: 20683 on 14919 degrees of freedom
Residual deviance: 14544 on 14856 degrees of freedom
AIC: 14672
Number of Fisher Scoring iterations: 5
VIF
GSEGRD 1.522526
LowerLimitAge 2.252678
BLS_FEDERAL_OtherSep_Rate 1.341059
BLS_FEDERAL_Quits_Rate 1.213219
BLS_FEDERAL_JobOpenings_Level 1.090067
LOSSqrt 1.731243
SEPCount_EFDATE_OCCLog 1.105098
AGELVL_B 1.164971
AGELVL_C 1.660784
AGELVL_E 1.405500
AGELVL_F 1.279617
AGELVL_G 1.266914
AGELVL_H 1.357709
AGELVL_I 1.514000
LOC_01 1.062121
LOC_02 1.034990
LOC_04 1.124417
LOC_06 1.260015
LOC_08 1.117082
LOC_13 1.140145
LOC_15 1.075570
LOC_17 1.081393
LOC_18 1.032244
LOC_19 1.019707
LOC_20 1.030516
LOC_22 1.043295
LOC_24 1.301291
LOC_25 1.048858
LOC_26 1.049178
LOC_27 1.035641
LOC_28 1.030433
LOC_29 1.067263
LOC_30 1.041969
LOC_31 1.019269
LOC_32 1.034950
LOC_33 1.009094
LOC_34 1.048058
LOC_35 1.098709
LOC_37 1.082421
LOC_38 1.017943
LOC_39 1.089478
LOC_40 1.069107
LOC_41 1.051718
LOC_42 1.086052
LOC_44 1.009007
LOC_46 1.040244
LOC_47 1.042137
LOC_48 1.249661
LOC_49 1.042196
LOC_50 1.006303
LOC_51 1.335636
LOC_53 1.118108
LOC_55 1.034902
TOA_15 1.403673
TOA_20 1.027479
TOA_30 1.222618
TOA_32 1.005622
TOA_35 1.076003
TOA_38 1.164145
TOA_40 1.057360
TOA_42 1.010300
TOA_44 1.003330
PPGROUP_11 1.148870
Removed AFTER this step: LOC_31
Call:
glm(formula = fla, family = "binomial", data = OPMAnalysisDataNoFamBinary)
Deviance Residuals:
Min 1Q Median 3Q Max
-2.8220 -0.7400 -0.1370 0.8187 3.2319
Coefficients:
Estimate Std. Error z value Pr(>|z|)
(Intercept) 2.174834 0.335751 6.478 9.32e-11 ***
GSEGRD -0.065270 0.014548 -4.487 7.24e-06 ***
LowerLimitAge -0.044707 0.002618 -17.075 < 2e-16 ***
BLS_FEDERAL_OtherSep_Rate 3.293663 0.292898 11.245 < 2e-16 ***
BLS_FEDERAL_Quits_Rate 0.459655 0.312479 1.471 0.141292
BLS_FEDERAL_JobOpenings_Level 0.017229 0.001848 9.324 < 2e-16 ***
LOSSqrt -0.779830 0.022105 -35.279 < 2e-16 ***
SEPCount_EFDATE_OCCLog -0.031322 0.016072 -1.949 0.051309 .
AGELVL_B1 -0.583756 0.219630 -2.658 0.007863 **
AGELVL_C1 -0.166259 0.085298 -1.949 0.051278 .
AGELVL_E1 0.275588 0.067257 4.098 4.18e-05 ***
AGELVL_F1 0.491564 0.070017 7.021 2.21e-12 ***
AGELVL_G1 0.714140 0.073875 9.667 < 2e-16 ***
AGELVL_H1 0.848168 0.076151 11.138 < 2e-16 ***
AGELVL_I1 0.719631 0.081037 8.880 < 2e-16 ***
LOC_011 -0.223687 0.167273 -1.337 0.181139
LOC_021 0.147296 0.250692 0.588 0.556828
LOC_041 0.511489 0.136750 3.740 0.000184 ***
LOC_061 0.405804 0.087898 4.617 3.90e-06 ***
LOC_081 0.133079 0.125587 1.060 0.289301
LOC_131 0.156470 0.115430 1.356 0.175245
LOC_151 -0.076728 0.160612 -0.478 0.632847
LOC_171 -0.231045 0.144230 -1.602 0.109173
LOC_181 -0.240410 0.250248 -0.961 0.336709
LOC_191 0.201875 0.318920 0.633 0.526737
LOC_201 0.599908 0.255088 2.352 0.018684 *
LOC_221 -0.124012 0.206975 -0.599 0.549063
LOC_241 -0.117110 0.083228 -1.407 0.159397
LOC_251 0.136890 0.181002 0.756 0.449477
LOC_261 -0.081922 0.200394 -0.409 0.682684
LOC_271 0.276968 0.236914 1.169 0.242377
LOC_281 -0.139363 0.245034 -0.569 0.569528
LOC_291 -0.363032 0.168492 -2.155 0.031193 *
LOC_301 0.668152 0.239267 2.792 0.005230 **
LOC_321 0.218141 0.243027 0.898 0.369400
LOC_331 0.184898 0.517887 0.357 0.721074
LOC_341 -0.188135 0.194778 -0.966 0.334096
LOC_351 0.457378 0.150490 3.039 0.002372 **
LOC_371 0.055434 0.151875 0.365 0.715111
LOC_381 0.474504 0.365955 1.297 0.194762
LOC_391 -0.301770 0.142105 -2.124 0.033706 *
LOC_401 0.165250 0.164968 1.002 0.316483
LOC_411 0.102862 0.196128 0.524 0.599954
LOC_421 -0.306760 0.143064 -2.144 0.032016 *
LOC_441 0.328955 0.491012 0.670 0.502888
LOC_461 0.800001 0.253077 3.161 0.001572 **
LOC_471 -0.193804 0.209018 -0.927 0.353817
LOC_481 0.241054 0.092786 2.598 0.009378 **
LOC_491 -0.198217 0.207902 -0.953 0.340378
LOC_501 -0.427811 0.591872 -0.723 0.469796
LOC_511 -0.164311 0.079112 -2.077 0.037805 *
LOC_531 0.388116 0.128168 3.028 0.002460 **
LOC_551 -0.332900 0.249639 -1.334 0.182360
TOA_151 0.067301 0.069053 0.975 0.329748
TOA_201 1.126230 0.192721 5.844 5.10e-09 ***
TOA_301 0.344006 0.088299 3.896 9.78e-05 ***
TOA_321 0.417389 0.754249 0.553 0.580001
TOA_351 -0.539989 0.286379 -1.886 0.059352 .
TOA_381 0.330707 0.066921 4.942 7.74e-07 ***
TOA_401 -0.106096 0.175983 -0.603 0.546591
TOA_421 1.141237 0.441298 2.586 0.009707 **
TOA_441 2.158296 1.071961 2.013 0.044072 *
PPGROUP_111 -0.408003 0.116674 -3.497 0.000471 ***
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
(Dispersion parameter for binomial family taken to be 1)
Null deviance: 20683 on 14919 degrees of freedom
Residual deviance: 14544 on 14857 degrees of freedom
AIC: 14670
Number of Fisher Scoring iterations: 5
VIF
GSEGRD 1.518890
LowerLimitAge 2.252156
BLS_FEDERAL_OtherSep_Rate 1.340928
BLS_FEDERAL_Quits_Rate 1.213060
BLS_FEDERAL_JobOpenings_Level 1.089716
LOSSqrt 1.731182
SEPCount_EFDATE_OCCLog 1.104275
AGELVL_B 1.164884
AGELVL_C 1.660730
AGELVL_E 1.404908
AGELVL_F 1.279585
AGELVL_G 1.266846
AGELVL_H 1.357674
AGELVL_I 1.513987
LOC_01 1.060657
LOC_02 1.034190
LOC_04 1.121670
LOC_06 1.253316
LOC_08 1.114198
LOC_13 1.136800
LOC_15 1.073821
LOC_17 1.079438
LOC_18 1.031580
LOC_19 1.019240
LOC_20 1.029806
LOC_22 1.042166
LOC_24 1.295076
LOC_25 1.047698
LOC_26 1.048083
LOC_27 1.034792
LOC_28 1.029699
LOC_29 1.065633
LOC_30 1.041013
LOC_32 1.034146
LOC_33 1.008909
LOC_34 1.047016
LOC_35 1.096568
LOC_37 1.080494
LOC_38 1.017498
LOC_39 1.087323
LOC_40 1.067453
LOC_41 1.050386
LOC_42 1.084089
LOC_44 1.008818
LOC_46 1.039417
LOC_47 1.041114
LOC_48 1.243719
LOC_49 1.041140
LOC_50 1.006190
LOC_51 1.327837
LOC_53 1.115132
LOC_55 1.034160
TOA_15 1.403614
TOA_20 1.027442
TOA_30 1.222028
TOA_32 1.005622
TOA_35 1.075881
TOA_38 1.164063
TOA_40 1.057257
TOA_42 1.010168
TOA_44 1.003315
PPGROUP_11 1.148685
Removed AFTER this step: LOC_33
Call:
glm(formula = fla, family = "binomial", data = OPMAnalysisDataNoFamBinary)
Deviance Residuals:
Min 1Q Median 3Q Max
-2.8220 -0.7398 -0.1370 0.8188 3.2316
Coefficients:
Estimate Std. Error z value Pr(>|z|)
(Intercept) 2.178149 0.335638 6.490 8.61e-11 ***
GSEGRD -0.065393 0.014544 -4.496 6.92e-06 ***
LowerLimitAge -0.044698 0.002618 -17.074 < 2e-16 ***
BLS_FEDERAL_OtherSep_Rate 3.292928 0.292892 11.243 < 2e-16 ***
BLS_FEDERAL_Quits_Rate 0.459667 0.312468 1.471 0.141268
BLS_FEDERAL_JobOpenings_Level 0.017219 0.001848 9.319 < 2e-16 ***
LOSSqrt -0.779791 0.022104 -35.278 < 2e-16 ***
SEPCount_EFDATE_OCCLog -0.031339 0.016072 -1.950 0.051188 .
AGELVL_B1 -0.582697 0.219610 -2.653 0.007970 **
AGELVL_C1 -0.166112 0.085298 -1.947 0.051483 .
AGELVL_E1 0.275630 0.067256 4.098 4.16e-05 ***
AGELVL_F1 0.491601 0.070015 7.021 2.20e-12 ***
AGELVL_G1 0.714097 0.073874 9.666 < 2e-16 ***
AGELVL_H1 0.848157 0.076151 11.138 < 2e-16 ***
AGELVL_I1 0.719640 0.081034 8.881 < 2e-16 ***
LOC_011 -0.225152 0.167226 -1.346 0.178176
LOC_021 0.145853 0.250657 0.582 0.560647
LOC_041 0.510020 0.136686 3.731 0.000190 ***
LOC_061 0.404343 0.087804 4.605 4.12e-06 ***
LOC_081 0.131719 0.125530 1.049 0.294036
LOC_131 0.155114 0.115368 1.345 0.178780
LOC_151 -0.078094 0.160565 -0.486 0.626705
LOC_171 -0.232442 0.144178 -1.612 0.106922
LOC_181 -0.242022 0.250208 -0.967 0.333402
LOC_191 0.200212 0.318896 0.628 0.530116
LOC_201 0.598435 0.255057 2.346 0.018962 *
LOC_221 -0.125495 0.206933 -0.606 0.544214
LOC_241 -0.118327 0.083159 -1.423 0.154763
LOC_251 0.135506 0.180962 0.749 0.453974
LOC_261 -0.083512 0.200345 -0.417 0.676794
LOC_271 0.275370 0.236878 1.163 0.245032
LOC_281 -0.140861 0.244998 -0.575 0.565327
LOC_291 -0.364553 0.168441 -2.164 0.030443 *
LOC_301 0.666624 0.239227 2.787 0.005327 **
LOC_321 0.216495 0.242986 0.891 0.372941
LOC_341 -0.189570 0.194739 -0.973 0.330325
LOC_351 0.455938 0.150436 3.031 0.002439 **
LOC_371 0.053964 0.151819 0.355 0.722253
LOC_381 0.472926 0.365933 1.292 0.196225
LOC_391 -0.303293 0.142043 -2.135 0.032743 *
LOC_401 0.163858 0.164920 0.994 0.320436
LOC_411 0.101301 0.196078 0.517 0.605409
LOC_421 -0.308226 0.143007 -2.155 0.031137 *
LOC_441 0.327418 0.490988 0.667 0.504864
LOC_461 0.798439 0.253041 3.155 0.001603 **
LOC_471 -0.195356 0.208974 -0.935 0.349873
LOC_481 0.239630 0.092700 2.585 0.009738 **
LOC_491 -0.199677 0.207861 -0.961 0.336738
LOC_501 -0.429401 0.591866 -0.726 0.468144
LOC_511 -0.165605 0.079030 -2.095 0.036129 *
LOC_531 0.386676 0.128104 3.018 0.002541 **
LOC_551 -0.334612 0.249600 -1.341 0.180053
TOA_151 0.067115 0.069050 0.972 0.331057
TOA_201 1.125945 0.192719 5.842 5.14e-09 ***
TOA_301 0.343578 0.088290 3.891 9.96e-05 ***
TOA_321 0.417539 0.754225 0.554 0.579852
TOA_351 -0.540798 0.286368 -1.888 0.058963 .
TOA_381 0.331547 0.066882 4.957 7.15e-07 ***
TOA_401 -0.106421 0.175983 -0.605 0.545364
TOA_421 1.143755 0.441206 2.592 0.009533 **
TOA_441 2.157351 1.071955 2.013 0.044163 *
PPGROUP_111 -0.407918 0.116673 -3.496 0.000472 ***
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
(Dispersion parameter for binomial family taken to be 1)
Null deviance: 20683 on 14919 degrees of freedom
Residual deviance: 14545 on 14858 degrees of freedom
AIC: 14669
Number of Fisher Scoring iterations: 5
VIF
GSEGRD 1.518055
LowerLimitAge 2.251755
BLS_FEDERAL_OtherSep_Rate 1.340828
BLS_FEDERAL_Quits_Rate 1.213043
BLS_FEDERAL_JobOpenings_Level 1.089487
LOSSqrt 1.731028
SEPCount_EFDATE_OCCLog 1.104286
AGELVL_B 1.164643
AGELVL_C 1.660683
AGELVL_E 1.404911
AGELVL_F 1.279578
AGELVL_G 1.266801
AGELVL_H 1.357583
AGELVL_I 1.513874
LOC_01 1.060022
LOC_02 1.033925
LOC_04 1.120672
LOC_06 1.250628
LOC_08 1.113189
LOC_13 1.135586
LOC_15 1.073225
LOC_17 1.078652
LOC_18 1.031249
LOC_19 1.019023
LOC_20 1.029539
LOC_22 1.041753
LOC_24 1.292937
LOC_25 1.047222
LOC_26 1.047574
LOC_27 1.034425
LOC_28 1.029400
LOC_29 1.064960
LOC_30 1.040684
LOC_32 1.033776
LOC_34 1.046575
LOC_35 1.095791
LOC_37 1.079713
LOC_38 1.017348
LOC_39 1.086352
LOC_40 1.066869
LOC_41 1.049868
LOC_42 1.083202
LOC_44 1.008740
LOC_46 1.039109
LOC_47 1.040667
LOC_48 1.241450
LOC_49 1.040743
LOC_50 1.006133
LOC_51 1.325094
LOC_53 1.114044
LOC_55 1.033783
TOA_15 1.403502
TOA_20 1.027424
TOA_30 1.221805
TOA_32 1.005622
TOA_35 1.075805
TOA_38 1.162650
TOA_40 1.057228
TOA_42 1.009962
TOA_44 1.003309
PPGROUP_11 1.148697
Removed AFTER this step: LOC_37
Call:
glm(formula = fla, family = "binomial", data = OPMAnalysisDataNoFamBinary)
Deviance Residuals:
Min 1Q Median 3Q Max
-2.8219 -0.7402 -0.1370 0.8190 3.2308
Coefficients:
Estimate Std. Error z value Pr(>|z|)
(Intercept) 2.184472 0.335177 6.517 7.15e-11 ***
GSEGRD -0.065714 0.014518 -4.526 6.00e-06 ***
LowerLimitAge -0.044682 0.002618 -17.070 < 2e-16 ***
BLS_FEDERAL_OtherSep_Rate 3.292797 0.292891 11.242 < 2e-16 ***
BLS_FEDERAL_Quits_Rate 0.463031 0.312323 1.483 0.138197
BLS_FEDERAL_JobOpenings_Level 0.017214 0.001848 9.317 < 2e-16 ***
LOSSqrt -0.779881 0.022103 -35.284 < 2e-16 ***
SEPCount_EFDATE_OCCLog -0.031132 0.016061 -1.938 0.052585 .
AGELVL_B1 -0.583580 0.219585 -2.658 0.007869 **
AGELVL_C1 -0.166522 0.085290 -1.952 0.050888 .
AGELVL_E1 0.275812 0.067254 4.101 4.11e-05 ***
AGELVL_F1 0.491695 0.070012 7.023 2.17e-12 ***
AGELVL_G1 0.714074 0.073873 9.666 < 2e-16 ***
AGELVL_H1 0.848405 0.076148 11.142 < 2e-16 ***
AGELVL_I1 0.719540 0.081037 8.879 < 2e-16 ***
LOC_011 -0.229783 0.166725 -1.378 0.168137
LOC_021 0.140821 0.250270 0.563 0.573657
LOC_041 0.504960 0.135944 3.714 0.000204 ***
LOC_061 0.399635 0.086803 4.604 4.15e-06 ***
LOC_081 0.126955 0.124819 1.017 0.309097
LOC_131 0.150404 0.114609 1.312 0.189409
LOC_151 -0.082994 0.159976 -0.519 0.603908
LOC_171 -0.237067 0.143594 -1.651 0.098747 .
LOC_181 -0.246821 0.249846 -0.988 0.323204
LOC_191 0.195402 0.318614 0.613 0.539686
LOC_201 0.593483 0.254692 2.330 0.019796 *
LOC_221 -0.130318 0.206493 -0.631 0.527974
LOC_241 -0.122670 0.082260 -1.491 0.135896
LOC_251 0.131018 0.180527 0.726 0.467989
LOC_261 -0.088415 0.199881 -0.442 0.658247
LOC_271 0.270432 0.236480 1.144 0.252801
LOC_281 -0.145666 0.244637 -0.595 0.551549
LOC_291 -0.369413 0.167892 -2.200 0.027785 *
LOC_301 0.661550 0.238811 2.770 0.005602 **
LOC_321 0.211692 0.242619 0.873 0.382920
LOC_341 -0.194109 0.194326 -0.999 0.317853
LOC_351 0.450928 0.149777 3.011 0.002607 **
LOC_381 0.467967 0.365692 1.280 0.200659
LOC_391 -0.307993 0.141431 -2.178 0.029430 *
LOC_401 0.159026 0.164359 0.968 0.333270
LOC_411 0.096550 0.195625 0.494 0.621625
LOC_421 -0.312803 0.142428 -2.196 0.028076 *
LOC_441 0.322616 0.490774 0.657 0.510948
LOC_461 0.793074 0.252596 3.140 0.001691 **
LOC_471 -0.200193 0.208536 -0.960 0.337058
LOC_481 0.234751 0.091682 2.560 0.010453 *
LOC_491 -0.204432 0.207434 -0.986 0.324364
LOC_501 -0.434257 0.591725 -0.734 0.463020
LOC_511 -0.170166 0.077988 -2.182 0.029112 *
LOC_531 0.381892 0.127398 2.998 0.002721 **
LOC_551 -0.339445 0.249239 -1.362 0.173222
TOA_151 0.066885 0.069045 0.969 0.332684
TOA_201 1.125044 0.192710 5.838 5.28e-09 ***
TOA_301 0.341710 0.088139 3.877 0.000106 ***
TOA_321 0.417849 0.754189 0.554 0.579553
TOA_351 -0.541202 0.286335 -1.890 0.058745 .
TOA_381 0.331605 0.066883 4.958 7.12e-07 ***
TOA_401 -0.107477 0.175968 -0.611 0.541348
TOA_421 1.142201 0.441189 2.589 0.009628 **
TOA_441 2.153505 1.071909 2.009 0.044533 *
PPGROUP_111 -0.408061 0.116673 -3.497 0.000470 ***
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
(Dispersion parameter for binomial family taken to be 1)
Null deviance: 20683 on 14919 degrees of freedom
Residual deviance: 14545 on 14859 degrees of freedom
AIC: 14667
Number of Fisher Scoring iterations: 5
VIF
GSEGRD 1.512280
LowerLimitAge 2.251018
BLS_FEDERAL_OtherSep_Rate 1.340790
BLS_FEDERAL_Quits_Rate 1.211930
BLS_FEDERAL_JobOpenings_Level 1.089395
LOSSqrt 1.730883
SEPCount_EFDATE_OCCLog 1.102851
AGELVL_B 1.164450
AGELVL_C 1.660398
AGELVL_E 1.404859
AGELVL_F 1.279586
AGELVL_G 1.266799
AGELVL_H 1.357462
AGELVL_I 1.513758
LOC_01 1.053598
LOC_02 1.030629
LOC_04 1.108536
LOC_06 1.222225
LOC_08 1.100523
LOC_13 1.120631
LOC_15 1.065337
LOC_17 1.069896
LOC_18 1.028260
LOC_19 1.017193
LOC_20 1.026470
LOC_22 1.037291
LOC_24 1.265115
LOC_25 1.042141
LOC_26 1.042619
LOC_27 1.030876
LOC_28 1.026272
LOC_29 1.057965
LOC_30 1.036981
LOC_32 1.030589
LOC_34 1.042095
LOC_35 1.086186
LOC_38 1.015875
LOC_39 1.076969
LOC_40 1.059639
LOC_41 1.045000
LOC_42 1.074458
LOC_44 1.007978
LOC_46 1.035415
LOC_47 1.036268
LOC_48 1.214284
LOC_49 1.036447
LOC_50 1.005599
LOC_51 1.290264
LOC_53 1.101776
LOC_55 1.030730
TOA_15 1.403390
TOA_20 1.027235
TOA_30 1.217459
TOA_32 1.005620
TOA_35 1.075770
TOA_38 1.162608
TOA_40 1.056915
TOA_42 1.009867
TOA_44 1.003207
PPGROUP_11 1.148730
Removed AFTER this step: LOC_26
Call:
glm(formula = fla, family = "binomial", data = OPMAnalysisDataNoFamBinary)
Deviance Residuals:
Min 1Q Median 3Q Max
-2.8217 -0.7400 -0.1370 0.8186 3.2318
Coefficients:
Estimate Std. Error z value Pr(>|z|)
(Intercept) 2.177785 0.334823 6.504 7.81e-11 ***
GSEGRD -0.065336 0.014492 -4.508 6.54e-06 ***
LowerLimitAge -0.044694 0.002617 -17.075 < 2e-16 ***
BLS_FEDERAL_OtherSep_Rate 3.293718 0.292885 11.246 < 2e-16 ***
BLS_FEDERAL_Quits_Rate 0.460713 0.312275 1.475 0.140120
BLS_FEDERAL_JobOpenings_Level 0.017215 0.001848 9.317 < 2e-16 ***
LOSSqrt -0.779992 0.022102 -35.291 < 2e-16 ***
SEPCount_EFDATE_OCCLog -0.031172 0.016061 -1.941 0.052270 .
AGELVL_B1 -0.583861 0.219563 -2.659 0.007833 **
AGELVL_C1 -0.166535 0.085287 -1.953 0.050862 .
AGELVL_E1 0.275792 0.067253 4.101 4.12e-05 ***
AGELVL_F1 0.491606 0.070012 7.022 2.19e-12 ***
AGELVL_G1 0.714010 0.073873 9.665 < 2e-16 ***
AGELVL_H1 0.848356 0.076150 11.141 < 2e-16 ***
AGELVL_I1 0.719471 0.081034 8.879 < 2e-16 ***
LOC_011 -0.225805 0.166476 -1.356 0.174978
LOC_021 0.145017 0.250089 0.580 0.562009
LOC_041 0.509115 0.135622 3.754 0.000174 ***
LOC_061 0.403704 0.086312 4.677 2.91e-06 ***
LOC_081 0.130881 0.124501 1.051 0.293148
LOC_131 0.154271 0.114273 1.350 0.177010
LOC_151 -0.079057 0.159731 -0.495 0.620644
LOC_171 -0.233109 0.143310 -1.627 0.103821
LOC_181 -0.242535 0.249656 -0.971 0.331311
LOC_191 0.199728 0.318453 0.627 0.530541
LOC_201 0.597655 0.254514 2.348 0.018863 *
LOC_221 -0.126179 0.206280 -0.612 0.540744
LOC_241 -0.119141 0.081869 -1.455 0.145598
LOC_251 0.134897 0.180310 0.748 0.454376
LOC_271 0.274773 0.236268 1.163 0.244841
LOC_281 -0.141487 0.244446 -0.579 0.562720
LOC_291 -0.365224 0.167620 -2.179 0.029340 *
LOC_301 0.665861 0.238616 2.791 0.005262 **
LOC_321 0.215979 0.242419 0.891 0.372966
LOC_341 -0.190084 0.194105 -0.979 0.327440
LOC_351 0.455090 0.149481 3.044 0.002331 **
LOC_381 0.472262 0.365556 1.292 0.196393
LOC_391 -0.303906 0.141126 -2.153 0.031284 *
LOC_401 0.163045 0.164110 0.994 0.320464
LOC_411 0.100774 0.195393 0.516 0.606028
LOC_421 -0.308857 0.142147 -2.173 0.029795 *
LOC_441 0.326790 0.490707 0.666 0.505439
LOC_461 0.797511 0.252398 3.160 0.001579 **
LOC_471 -0.196007 0.208316 -0.941 0.346751
LOC_481 0.238809 0.091221 2.618 0.008847 **
LOC_491 -0.200400 0.207233 -0.967 0.333530
LOC_501 -0.430106 0.591634 -0.727 0.467239
LOC_511 -0.166400 0.077517 -2.147 0.031822 *
LOC_531 0.385956 0.127067 3.037 0.002386 **
LOC_551 -0.335025 0.249030 -1.345 0.178522
TOA_151 0.067252 0.069041 0.974 0.330012
TOA_201 1.125435 0.192715 5.840 5.22e-09 ***
TOA_301 0.342393 0.088124 3.885 0.000102 ***
TOA_321 0.417338 0.754246 0.553 0.580046
TOA_351 -0.545449 0.286238 -1.906 0.056705 .
TOA_381 0.330550 0.066833 4.946 7.58e-07 ***
TOA_401 -0.107444 0.175955 -0.611 0.541443
TOA_421 1.143245 0.441175 2.591 0.009560 **
TOA_441 2.156214 1.071893 2.012 0.044263 *
PPGROUP_111 -0.408148 0.116678 -3.498 0.000469 ***
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
(Dispersion parameter for binomial family taken to be 1)
Null deviance: 20683 on 14919 degrees of freedom
Residual deviance: 14545 on 14860 degrees of freedom
AIC: 14665
Number of Fisher Scoring iterations: 5
VIF
GSEGRD 1.506995
LowerLimitAge 2.250918
BLS_FEDERAL_OtherSep_Rate 1.340706
BLS_FEDERAL_Quits_Rate 1.211608
BLS_FEDERAL_JobOpenings_Level 1.089375
LOSSqrt 1.730760
SEPCount_EFDATE_OCCLog 1.102818
AGELVL_B 1.164402
AGELVL_C 1.660390
AGELVL_E 1.404808
AGELVL_F 1.279548
AGELVL_G 1.266786
AGELVL_H 1.357452
AGELVL_I 1.513875
LOC_01 1.050524
LOC_02 1.029147
LOC_04 1.103224
LOC_06 1.208461
LOC_08 1.094939
LOC_13 1.114088
LOC_15 1.062019
LOC_17 1.065725
LOC_18 1.026702
LOC_19 1.016233
LOC_20 1.025059
LOC_22 1.035150
LOC_24 1.253144
LOC_25 1.039673
LOC_27 1.029097
LOC_28 1.024737
LOC_29 1.054584
LOC_30 1.035252
LOC_32 1.028939
LOC_34 1.039802
LOC_35 1.081890
LOC_38 1.015156
LOC_39 1.072350
LOC_40 1.056385
LOC_41 1.042503
LOC_42 1.070213
LOC_44 1.007605
LOC_46 1.033782
LOC_47 1.034125
LOC_48 1.202093
LOC_49 1.034435
LOC_50 1.005344
LOC_51 1.274819
LOC_53 1.096022
LOC_55 1.029062
TOA_15 1.403215
TOA_20 1.027209
TOA_30 1.217094
TOA_32 1.005618
TOA_35 1.074343
TOA_38 1.161137
TOA_40 1.056920
TOA_42 1.009835
TOA_44 1.003174
PPGROUP_11 1.148692
Removed AFTER this step: LOC_15
Call:
glm(formula = fla, family = "binomial", data = OPMAnalysisDataNoFamBinary)
Deviance Residuals:
Min 1Q Median 3Q Max
-2.8213 -0.7403 -0.1371 0.8185 3.2324
Coefficients:
Estimate Std. Error z value Pr(>|z|)
(Intercept) 2.167100 0.334112 6.486 8.81e-11 ***
GSEGRD -0.064988 0.014474 -4.490 7.12e-06 ***
LowerLimitAge -0.044706 0.002617 -17.081 < 2e-16 ***
BLS_FEDERAL_OtherSep_Rate 3.295427 0.292864 11.252 < 2e-16 ***
BLS_FEDERAL_Quits_Rate 0.460022 0.312275 1.473 0.140715
BLS_FEDERAL_JobOpenings_Level 0.017221 0.001848 9.321 < 2e-16 ***
LOSSqrt -0.779825 0.022098 -35.290 < 2e-16 ***
SEPCount_EFDATE_OCCLog -0.031434 0.016053 -1.958 0.050212 .
AGELVL_B1 -0.582016 0.219528 -2.651 0.008020 **
AGELVL_C1 -0.166465 0.085283 -1.952 0.050948 .
AGELVL_E1 0.275444 0.067249 4.096 4.21e-05 ***
AGELVL_F1 0.491826 0.070009 7.025 2.14e-12 ***
AGELVL_G1 0.713842 0.073876 9.663 < 2e-16 ***
AGELVL_H1 0.848003 0.076146 11.136 < 2e-16 ***
AGELVL_I1 0.718703 0.081016 8.871 < 2e-16 ***
LOC_011 -0.220532 0.166130 -1.327 0.184354
LOC_021 0.150877 0.249787 0.604 0.545827
LOC_041 0.515111 0.135076 3.813 0.000137 ***
LOC_061 0.409032 0.085633 4.777 1.78e-06 ***
LOC_081 0.136450 0.123983 1.101 0.271090
LOC_131 0.159834 0.113715 1.406 0.159850
LOC_171 -0.227807 0.142909 -1.594 0.110919
LOC_181 -0.237452 0.249441 -0.952 0.341129
LOC_191 0.204964 0.318292 0.644 0.519609
LOC_201 0.603451 0.254227 2.374 0.017613 *
LOC_221 -0.120642 0.205971 -0.586 0.558060
LOC_241 -0.113956 0.081194 -1.404 0.160463
LOC_251 0.140003 0.180012 0.778 0.436721
LOC_271 0.280256 0.236002 1.188 0.235025
LOC_281 -0.136051 0.244189 -0.557 0.577423
LOC_291 -0.359645 0.167232 -2.151 0.031510 *
LOC_301 0.671827 0.238296 2.819 0.004813 **
LOC_321 0.221209 0.242185 0.913 0.361039
LOC_341 -0.184926 0.193819 -0.954 0.340026
LOC_351 0.461018 0.148993 3.094 0.001973 **
LOC_381 0.478059 0.365342 1.309 0.190696
LOC_391 -0.298616 0.140718 -2.122 0.033830 *
LOC_401 0.168755 0.163701 1.031 0.302600
LOC_411 0.106113 0.195093 0.544 0.586503
LOC_421 -0.303676 0.141758 -2.142 0.032177 *
LOC_441 0.332141 0.490624 0.677 0.498420
LOC_461 0.803656 0.252079 3.188 0.001432 **
LOC_471 -0.190606 0.208029 -0.916 0.359538
LOC_481 0.244468 0.090497 2.701 0.006905 **
LOC_491 -0.194813 0.206919 -0.941 0.346453
LOC_501 -0.424753 0.591543 -0.718 0.472731
LOC_511 -0.160960 0.076726 -2.098 0.035918 *
LOC_531 0.391562 0.126556 3.094 0.001975 **
LOC_551 -0.329849 0.248806 -1.326 0.184929
TOA_151 0.067198 0.069042 0.973 0.330409
TOA_201 1.125152 0.192690 5.839 5.25e-09 ***
TOA_301 0.344818 0.087979 3.919 8.88e-05 ***
TOA_321 0.416961 0.754295 0.553 0.580413
TOA_351 -0.542589 0.286165 -1.896 0.057951 .
TOA_381 0.331979 0.066769 4.972 6.62e-07 ***
TOA_401 -0.105069 0.175882 -0.597 0.550252
TOA_421 1.144330 0.441190 2.594 0.009494 **
TOA_441 2.160675 1.071851 2.016 0.043817 *
PPGROUP_111 -0.406781 0.116648 -3.487 0.000488 ***
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
(Dispersion parameter for binomial family taken to be 1)
Null deviance: 20683 on 14919 degrees of freedom
Residual deviance: 14545 on 14861 degrees of freedom
AIC: 14663
Number of Fisher Scoring iterations: 5
VIF
GSEGRD 1.503479
LowerLimitAge 2.250805
BLS_FEDERAL_OtherSep_Rate 1.340598
BLS_FEDERAL_Quits_Rate 1.211633
BLS_FEDERAL_JobOpenings_Level 1.089331
LOSSqrt 1.730392
SEPCount_EFDATE_OCCLog 1.101626
AGELVL_B 1.163997
AGELVL_C 1.660373
AGELVL_E 1.404599
AGELVL_F 1.279487
AGELVL_G 1.266678
AGELVL_H 1.357266
AGELVL_I 1.513259
LOC_01 1.046210
LOC_02 1.026840
LOC_04 1.094413
LOC_06 1.189597
LOC_08 1.085976
LOC_13 1.103284
LOC_17 1.059736
LOC_18 1.024950
LOC_19 1.015103
LOC_20 1.022888
LOC_22 1.032090
LOC_24 1.232548
LOC_25 1.036251
LOC_27 1.026820
LOC_28 1.022660
LOC_29 1.049793
LOC_30 1.032611
LOC_32 1.026972
LOC_34 1.036780
LOC_35 1.074937
LOC_38 1.014109
LOC_39 1.066168
LOC_40 1.051144
LOC_41 1.039316
LOC_42 1.064367
LOC_44 1.007114
LOC_46 1.031278
LOC_47 1.031270
LOC_48 1.183171
LOC_49 1.031354
LOC_50 1.005006
LOC_51 1.249082
LOC_53 1.087281
LOC_55 1.027229
TOA_15 1.403272
TOA_20 1.027213
TOA_30 1.213379
TOA_32 1.005617
TOA_35 1.073867
TOA_38 1.158998
TOA_40 1.056125
TOA_42 1.009808
TOA_44 1.003102
PPGROUP_11 1.148037
Removed AFTER this step: LOC_41
Call:
glm(formula = fla, family = "binomial", data = OPMAnalysisDataNoFamBinary)
Deviance Residuals:
Min 1Q Median 3Q Max
-2.8220 -0.7401 -0.1371 0.8186 3.2315
Coefficients:
Estimate Std. Error z value Pr(>|z|)
(Intercept) 2.178870 0.333420 6.535 6.36e-11 ***
GSEGRD -0.065464 0.014448 -4.531 5.87e-06 ***
LowerLimitAge -0.044689 0.002617 -17.077 < 2e-16 ***
BLS_FEDERAL_OtherSep_Rate 3.294949 0.292863 11.251 < 2e-16 ***
BLS_FEDERAL_Quits_Rate 0.460686 0.312265 1.475 0.140131
BLS_FEDERAL_JobOpenings_Level 0.017222 0.001848 9.322 < 2e-16 ***
LOSSqrt -0.779652 0.022096 -35.285 < 2e-16 ***
SEPCount_EFDATE_OCCLog -0.031881 0.016033 -1.988 0.046767 *
AGELVL_B1 -0.582450 0.219560 -2.653 0.007983 **
AGELVL_C1 -0.167140 0.085275 -1.960 0.049995 *
AGELVL_E1 0.275276 0.067249 4.093 4.25e-05 ***
AGELVL_F1 0.492073 0.070005 7.029 2.08e-12 ***
AGELVL_G1 0.713980 0.073875 9.665 < 2e-16 ***
AGELVL_H1 0.848069 0.076138 11.139 < 2e-16 ***
AGELVL_I1 0.719032 0.081014 8.875 < 2e-16 ***
LOC_011 -0.225058 0.165919 -1.356 0.174963
LOC_021 0.145821 0.249603 0.584 0.559077
LOC_041 0.510348 0.134785 3.786 0.000153 ***
LOC_061 0.404403 0.085207 4.746 2.07e-06 ***
LOC_081 0.131928 0.123700 1.067 0.286191
LOC_131 0.155365 0.113413 1.370 0.170716
LOC_171 -0.232225 0.142671 -1.628 0.103590
LOC_181 -0.242055 0.249284 -0.971 0.331549
LOC_191 0.199885 0.318132 0.628 0.529802
LOC_201 0.598674 0.254054 2.356 0.018449 *
LOC_221 -0.125437 0.205772 -0.610 0.542131
LOC_241 -0.118063 0.080841 -1.460 0.144168
LOC_251 0.135587 0.179818 0.754 0.450835
LOC_271 0.275300 0.235823 1.167 0.243049
LOC_281 -0.140905 0.244009 -0.577 0.563631
LOC_291 -0.364353 0.167004 -2.182 0.029132 *
LOC_301 0.666626 0.238084 2.800 0.005111 **
LOC_321 0.216197 0.241998 0.893 0.371652
LOC_341 -0.189343 0.193646 -0.978 0.328182
LOC_351 0.456394 0.148745 3.068 0.002153 **
LOC_381 0.472684 0.365169 1.294 0.195518
LOC_391 -0.303184 0.140464 -2.158 0.030893 *
LOC_401 0.164191 0.163476 1.004 0.315197
LOC_421 -0.307952 0.141536 -2.176 0.029572 *
LOC_441 0.327239 0.490482 0.667 0.504659
LOC_461 0.798665 0.251894 3.171 0.001521 **
LOC_471 -0.195362 0.207841 -0.940 0.347238
LOC_481 0.239898 0.090104 2.662 0.007757 **
LOC_491 -0.199520 0.206729 -0.965 0.334480
LOC_501 -0.429365 0.591445 -0.726 0.467864
LOC_511 -0.165273 0.076318 -2.166 0.030344 *
LOC_531 0.386865 0.126254 3.064 0.002183 **
LOC_551 -0.334747 0.248628 -1.346 0.178182
TOA_151 0.066742 0.069034 0.967 0.333643
TOA_201 1.124479 0.192695 5.836 5.36e-09 ***
TOA_301 0.344001 0.087958 3.911 9.19e-05 ***
TOA_321 0.417067 0.754190 0.553 0.580263
TOA_351 -0.544156 0.286159 -1.902 0.057225 .
TOA_381 0.332126 0.066773 4.974 6.56e-07 ***
TOA_401 -0.106075 0.175879 -0.603 0.546433
TOA_421 1.145463 0.441146 2.597 0.009416 **
TOA_441 2.157406 1.071822 2.013 0.044131 *
PPGROUP_111 -0.407702 0.116632 -3.496 0.000473 ***
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
(Dispersion parameter for binomial family taken to be 1)
Null deviance: 20683 on 14919 degrees of freedom
Residual deviance: 14545 on 14862 degrees of freedom
AIC: 14661
Number of Fisher Scoring iterations: 5
VIF
GSEGRD 1.497918
LowerLimitAge 2.250272
BLS_FEDERAL_OtherSep_Rate 1.340597
BLS_FEDERAL_Quits_Rate 1.211599
BLS_FEDERAL_JobOpenings_Level 1.089337
LOSSqrt 1.729903
SEPCount_EFDATE_OCCLog 1.098816
AGELVL_B 1.164069
AGELVL_C 1.660025
AGELVL_E 1.404572
AGELVL_F 1.279456
AGELVL_G 1.266619
AGELVL_H 1.357234
AGELVL_I 1.512974
LOC_01 1.043598
LOC_02 1.025422
LOC_04 1.089839
LOC_06 1.177903
LOC_08 1.081119
LOC_13 1.097553
LOC_17 1.056338
LOC_18 1.023783
LOC_19 1.014235
LOC_20 1.021675
LOC_22 1.030213
LOC_24 1.221963
LOC_25 1.034158
LOC_27 1.025298
LOC_28 1.021304
LOC_29 1.047001
LOC_30 1.030953
LOC_32 1.025495
LOC_34 1.034977
LOC_35 1.071456
LOC_38 1.013371
LOC_39 1.062398
LOC_40 1.048404
LOC_42 1.061124
LOC_44 1.006775
LOC_46 1.029916
LOC_47 1.029458
LOC_48 1.173022
LOC_49 1.029562
LOC_50 1.004802
LOC_51 1.235817
LOC_53 1.082251
LOC_55 1.025900
TOA_15 1.402979
TOA_20 1.027189
TOA_30 1.212992
TOA_32 1.005617
TOA_35 1.073894
TOA_38 1.158938
TOA_40 1.056016
TOA_42 1.009783
TOA_44 1.003071
PPGROUP_11 1.147824
Removed AFTER this step: TOA_32
Call:
glm(formula = fla, family = "binomial", data = OPMAnalysisDataNoFamBinary)
Deviance Residuals:
Min 1Q Median 3Q Max
-2.8217 -0.7405 -0.1372 0.8185 3.2315
Coefficients:
Estimate Std. Error z value Pr(>|z|)
(Intercept) 2.176247 0.333377 6.528 6.67e-11 ***
GSEGRD -0.065233 0.014441 -4.517 6.27e-06 ***
LowerLimitAge -0.044682 0.002617 -17.074 < 2e-16 ***
BLS_FEDERAL_OtherSep_Rate 3.295024 0.292860 11.251 < 2e-16 ***
BLS_FEDERAL_Quits_Rate 0.461875 0.312251 1.479 0.139092
BLS_FEDERAL_JobOpenings_Level 0.017209 0.001847 9.315 < 2e-16 ***
LOSSqrt -0.779712 0.022095 -35.288 < 2e-16 ***
SEPCount_EFDATE_OCCLog -0.031981 0.016032 -1.995 0.046059 *
AGELVL_B1 -0.582063 0.219556 -2.651 0.008023 **
AGELVL_C1 -0.167290 0.085273 -1.962 0.049784 *
AGELVL_E1 0.275584 0.067247 4.098 4.17e-05 ***
AGELVL_F1 0.491817 0.070001 7.026 2.13e-12 ***
AGELVL_G1 0.714831 0.073860 9.678 < 2e-16 ***
AGELVL_H1 0.847644 0.076133 11.134 < 2e-16 ***
AGELVL_I1 0.718729 0.081013 8.872 < 2e-16 ***
LOC_011 -0.225053 0.165912 -1.356 0.174951
LOC_021 0.145989 0.249587 0.585 0.558601
LOC_041 0.510591 0.134781 3.788 0.000152 ***
LOC_061 0.405592 0.085180 4.762 1.92e-06 ***
LOC_081 0.131946 0.123694 1.067 0.286101
LOC_131 0.155377 0.113409 1.370 0.170669
LOC_171 -0.232178 0.142666 -1.627 0.103648
LOC_181 -0.241865 0.249271 -0.970 0.331902
LOC_191 0.199976 0.318124 0.629 0.529604
LOC_201 0.598850 0.254046 2.357 0.018411 *
LOC_221 -0.125316 0.205764 -0.609 0.542506
LOC_241 -0.118207 0.080837 -1.462 0.143666
LOC_251 0.135651 0.179811 0.754 0.450602
LOC_271 0.275515 0.235814 1.168 0.242662
LOC_281 -0.139292 0.244122 -0.571 0.568282
LOC_291 -0.364184 0.166997 -2.181 0.029199 *
LOC_301 0.666838 0.238071 2.801 0.005094 **
LOC_321 0.216301 0.241985 0.894 0.371397
LOC_341 -0.189289 0.193641 -0.978 0.328310
LOC_351 0.456627 0.148738 3.070 0.002141 **
LOC_381 0.472784 0.365140 1.295 0.195389
LOC_391 -0.301911 0.140495 -2.149 0.031641 *
LOC_401 0.164374 0.163470 1.006 0.314643
LOC_421 -0.307922 0.141533 -2.176 0.029583 *
LOC_441 0.327257 0.490465 0.667 0.504620
LOC_461 0.799018 0.251882 3.172 0.001513 **
LOC_471 -0.195241 0.207835 -0.939 0.347523
LOC_481 0.239965 0.090102 2.663 0.007739 **
LOC_491 -0.199426 0.206719 -0.965 0.334687
LOC_501 -0.429463 0.591412 -0.726 0.467737
LOC_511 -0.163443 0.076245 -2.144 0.032061 *
LOC_531 0.386944 0.126251 3.065 0.002178 **
LOC_551 -0.334569 0.248620 -1.346 0.178397
TOA_151 0.066393 0.069031 0.962 0.336158
TOA_201 1.124169 0.192691 5.834 5.41e-09 ***
TOA_301 0.343255 0.087945 3.903 9.50e-05 ***
TOA_351 -0.543946 0.286153 -1.901 0.057316 .
TOA_381 0.331762 0.066770 4.969 6.74e-07 ***
TOA_401 -0.106546 0.175870 -0.606 0.544633
TOA_421 1.144861 0.441131 2.595 0.009451 **
TOA_441 2.156808 1.071823 2.012 0.044190 *
PPGROUP_111 -0.407082 0.116626 -3.490 0.000482 ***
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
(Dispersion parameter for binomial family taken to be 1)
Null deviance: 20683 on 14919 degrees of freedom
Residual deviance: 14546 on 14863 degrees of freedom
AIC: 14660
Number of Fisher Scoring iterations: 5
VIF
GSEGRD 1.496766
LowerLimitAge 2.250294
BLS_FEDERAL_OtherSep_Rate 1.340601
BLS_FEDERAL_Quits_Rate 1.211541
BLS_FEDERAL_JobOpenings_Level 1.089152
LOSSqrt 1.729923
SEPCount_EFDATE_OCCLog 1.098668
AGELVL_B 1.164040
AGELVL_C 1.660037
AGELVL_E 1.404476
AGELVL_F 1.279409
AGELVL_G 1.266206
AGELVL_H 1.357132
AGELVL_I 1.512955
LOC_01 1.043600
LOC_02 1.025423
LOC_04 1.089832
LOC_06 1.177227
LOC_08 1.081124
LOC_13 1.097555
LOC_17 1.056339
LOC_18 1.023781
LOC_19 1.014235
LOC_20 1.021674
LOC_22 1.030213
LOC_24 1.221957
LOC_25 1.034159
LOC_27 1.025297
LOC_28 1.021065
LOC_29 1.046999
LOC_30 1.030954
LOC_32 1.025496
LOC_34 1.034975
LOC_35 1.071452
LOC_38 1.013372
LOC_39 1.061973
LOC_40 1.048400
LOC_42 1.061125
LOC_44 1.006775
LOC_46 1.029913
LOC_47 1.029457
LOC_48 1.173022
LOC_49 1.029563
LOC_50 1.004802
LOC_51 1.233545
LOC_53 1.082250
LOC_55 1.025898
TOA_15 1.402889
TOA_20 1.027183
TOA_30 1.212698
TOA_35 1.073886
TOA_38 1.158843
TOA_40 1.056003
TOA_42 1.009778
TOA_44 1.003070
PPGROUP_11 1.147716
Removed AFTER this step: LOC_28
Call:
glm(formula = fla, family = "binomial", data = OPMAnalysisDataNoFamBinary)
Deviance Residuals:
Min 1Q Median 3Q Max
-2.8215 -0.7405 -0.1380 0.8185 3.2322
Coefficients:
Estimate Std. Error z value Pr(>|z|)
(Intercept) 2.169054 0.333129 6.511 7.46e-11 ***
GSEGRD -0.064945 0.014431 -4.500 6.79e-06 ***
LowerLimitAge -0.044685 0.002617 -17.075 < 2e-16 ***
BLS_FEDERAL_OtherSep_Rate 3.294316 0.292857 11.249 < 2e-16 ***
BLS_FEDERAL_Quits_Rate 0.462407 0.312235 1.481 0.138617
BLS_FEDERAL_JobOpenings_Level 0.017205 0.001847 9.313 < 2e-16 ***
LOSSqrt -0.779818 0.022095 -35.293 < 2e-16 ***
SEPCount_EFDATE_OCCLog -0.031867 0.016030 -1.988 0.046810 *
AGELVL_B1 -0.580881 0.219532 -2.646 0.008145 **
AGELVL_C1 -0.166532 0.085262 -1.953 0.050798 .
AGELVL_E1 0.276004 0.067240 4.105 4.05e-05 ***
AGELVL_F1 0.492085 0.069995 7.030 2.06e-12 ***
AGELVL_G1 0.715377 0.073857 9.686 < 2e-16 ***
AGELVL_H1 0.847943 0.076130 11.138 < 2e-16 ***
AGELVL_I1 0.718427 0.081014 8.868 < 2e-16 ***
LOC_011 -0.221452 0.165793 -1.336 0.181642
LOC_021 0.149896 0.249494 0.601 0.547974
LOC_041 0.514401 0.134620 3.821 0.000133 ***
LOC_061 0.409229 0.084942 4.818 1.45e-06 ***
LOC_081 0.135539 0.123535 1.097 0.272567
LOC_131 0.158946 0.113238 1.404 0.160427
LOC_171 -0.228645 0.142535 -1.604 0.108684
LOC_181 -0.238148 0.249191 -0.956 0.339231
LOC_191 0.203862 0.318063 0.641 0.521556
LOC_201 0.602653 0.253968 2.373 0.017647 *
LOC_221 -0.121581 0.205665 -0.591 0.554412
LOC_241 -0.114905 0.080629 -1.425 0.154128
LOC_251 0.139141 0.179713 0.774 0.438786
LOC_271 0.279380 0.235715 1.185 0.235922
LOC_291 -0.360457 0.166870 -2.160 0.030764 *
LOC_301 0.670852 0.237976 2.819 0.004818 **
LOC_321 0.220143 0.241894 0.910 0.362780
LOC_341 -0.185793 0.193545 -0.960 0.337080
LOC_351 0.460377 0.148596 3.098 0.001947 **
LOC_381 0.476738 0.365090 1.306 0.191617
LOC_391 -0.298290 0.140353 -2.125 0.033563 *
LOC_401 0.168039 0.163349 1.029 0.303616
LOC_421 -0.304488 0.141407 -2.153 0.031298 *
LOC_441 0.331054 0.490464 0.675 0.499688
LOC_461 0.802992 0.251793 3.189 0.001427 **
LOC_471 -0.191499 0.207734 -0.922 0.356608
LOC_481 0.243618 0.089875 2.711 0.006716 **
LOC_491 -0.195699 0.206621 -0.947 0.343567
LOC_501 -0.425743 0.591387 -0.720 0.471583
LOC_511 -0.159954 0.075996 -2.105 0.035313 *
LOC_531 0.390625 0.126090 3.098 0.001948 **
LOC_551 -0.330756 0.248533 -1.331 0.183243
TOA_151 0.066255 0.069034 0.960 0.337180
TOA_201 1.125226 0.192682 5.840 5.23e-09 ***
TOA_301 0.344208 0.087937 3.914 9.07e-05 ***
TOA_351 -0.543791 0.286142 -1.900 0.057378 .
TOA_381 0.331520 0.066769 4.965 6.86e-07 ***
TOA_401 -0.105577 0.175858 -0.600 0.548270
TOA_421 1.146256 0.441115 2.599 0.009362 **
TOA_441 2.159517 1.071813 2.015 0.043923 *
PPGROUP_111 -0.406854 0.116629 -3.488 0.000486 ***
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
(Dispersion parameter for binomial family taken to be 1)
Null deviance: 20683 on 14919 degrees of freedom
Residual deviance: 14546 on 14864 degrees of freedom
AIC: 14658
Number of Fisher Scoring iterations: 5
VIF
GSEGRD 1.494861
LowerLimitAge 2.250410
BLS_FEDERAL_OtherSep_Rate 1.340539
BLS_FEDERAL_Quits_Rate 1.211511
BLS_FEDERAL_JobOpenings_Level 1.089136
LOSSqrt 1.729950
SEPCount_EFDATE_OCCLog 1.098513
AGELVL_B 1.163762
AGELVL_C 1.659602
AGELVL_E 1.404343
AGELVL_F 1.279406
AGELVL_G 1.266026
AGELVL_H 1.357184
AGELVL_I 1.512943
LOC_01 1.042083
LOC_02 1.024650
LOC_04 1.087148
LOC_06 1.170599
LOC_08 1.078309
LOC_13 1.094198
LOC_17 1.054332
LOC_18 1.023077
LOC_19 1.013767
LOC_20 1.020968
LOC_22 1.029161
LOC_24 1.215632
LOC_25 1.032952
LOC_27 1.024447
LOC_29 1.045386
LOC_30 1.030052
LOC_32 1.024698
LOC_34 1.033927
LOC_35 1.069356
LOC_38 1.013003
LOC_39 1.059792
LOC_40 1.046769
LOC_42 1.059184
LOC_44 1.006589
LOC_46 1.029125
LOC_47 1.028426
LOC_48 1.167075
LOC_49 1.028528
LOC_50 1.004679
LOC_51 1.225560
LOC_53 1.079402
LOC_55 1.025149
TOA_15 1.402840
TOA_20 1.027086
TOA_30 1.212261
TOA_35 1.073691
TOA_38 1.158770
TOA_40 1.055898
TOA_42 1.009745
TOA_44 1.003050
PPGROUP_11 1.147690
Removed AFTER this step: LOC_22
Call:
glm(formula = fla, family = "binomial", data = OPMAnalysisDataNoFamBinary)
Deviance Residuals:
Min 1Q Median 3Q Max
-2.8203 -0.7409 -0.1380 0.8186 3.2332
Coefficients:
Estimate Std. Error z value Pr(>|z|)
(Intercept) 2.161288 0.332870 6.493 8.42e-11 ***
GSEGRD -0.064562 0.014417 -4.478 7.52e-06 ***
LowerLimitAge -0.044713 0.002617 -17.088 < 2e-16 ***
BLS_FEDERAL_OtherSep_Rate 3.294886 0.292855 11.251 < 2e-16 ***
BLS_FEDERAL_Quits_Rate 0.460607 0.312216 1.475 0.140136
BLS_FEDERAL_JobOpenings_Level 0.017199 0.001847 9.310 < 2e-16 ***
LOSSqrt -0.779801 0.022096 -35.292 < 2e-16 ***
SEPCount_EFDATE_OCCLog -0.031724 0.016029 -1.979 0.047788 *
AGELVL_B1 -0.579085 0.219504 -2.638 0.008336 **
AGELVL_C1 -0.166917 0.085256 -1.958 0.050251 .
AGELVL_E1 0.275786 0.067237 4.102 4.10e-05 ***
AGELVL_F1 0.492026 0.070000 7.029 2.08e-12 ***
AGELVL_G1 0.715670 0.073853 9.690 < 2e-16 ***
AGELVL_H1 0.848281 0.076129 11.143 < 2e-16 ***
AGELVL_I1 0.718516 0.081011 8.869 < 2e-16 ***
LOC_011 -0.217237 0.165633 -1.312 0.189670
LOC_021 0.154550 0.249355 0.620 0.535390
LOC_041 0.518977 0.134396 3.862 0.000113 ***
LOC_061 0.413593 0.084618 4.888 1.02e-06 ***
LOC_081 0.139877 0.123311 1.134 0.256652
LOC_131 0.163207 0.113006 1.444 0.148672
LOC_171 -0.224452 0.142355 -1.577 0.114863
LOC_181 -0.233653 0.249068 -0.938 0.348188
LOC_191 0.208435 0.317976 0.656 0.512142
LOC_201 0.607172 0.253847 2.392 0.016762 *
LOC_241 -0.110925 0.080345 -1.381 0.167397
LOC_251 0.143281 0.179572 0.798 0.424928
LOC_271 0.283939 0.235582 1.205 0.228099
LOC_291 -0.355953 0.166688 -2.135 0.032724 *
LOC_301 0.675637 0.237836 2.841 0.004500 **
LOC_321 0.224748 0.241765 0.930 0.352573
LOC_341 -0.181566 0.193407 -0.939 0.347844
LOC_351 0.464899 0.148396 3.133 0.001731 **
LOC_381 0.481601 0.364993 1.319 0.187008
LOC_391 -0.294018 0.140163 -2.098 0.035932 *
LOC_401 0.172461 0.163178 1.057 0.290560
LOC_421 -0.300337 0.141229 -2.127 0.033453 *
LOC_441 0.335543 0.490436 0.684 0.493866
LOC_461 0.807800 0.251665 3.210 0.001328 **
LOC_471 -0.187048 0.207590 -0.901 0.367566
LOC_481 0.247992 0.089568 2.769 0.005627 **
LOC_491 -0.191196 0.206470 -0.926 0.354433
LOC_501 -0.421389 0.591355 -0.713 0.476104
LOC_511 -0.155766 0.075658 -2.059 0.039513 *
LOC_531 0.395079 0.125863 3.139 0.001695 **
LOC_551 -0.326195 0.248409 -1.313 0.189136
TOA_151 0.066241 0.069036 0.960 0.337299
TOA_201 1.123629 0.192663 5.832 5.47e-09 ***
TOA_301 0.345404 0.087910 3.929 8.53e-05 ***
TOA_351 -0.541986 0.286141 -1.894 0.058209 .
TOA_381 0.331559 0.066770 4.966 6.84e-07 ***
TOA_401 -0.105442 0.175862 -0.600 0.548791
TOA_421 1.145778 0.441115 2.597 0.009392 **
TOA_441 2.162627 1.071804 2.018 0.043618 *
PPGROUP_111 -0.406502 0.116642 -3.485 0.000492 ***
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
(Dispersion parameter for binomial family taken to be 1)
Null deviance: 20683 on 14919 degrees of freedom
Residual deviance: 14546 on 14865 degrees of freedom
AIC: 14656
Number of Fisher Scoring iterations: 5
VIF
GSEGRD 1.491860
LowerLimitAge 2.249857
BLS_FEDERAL_OtherSep_Rate 1.340624
BLS_FEDERAL_Quits_Rate 1.211398
BLS_FEDERAL_JobOpenings_Level 1.089165
LOSSqrt 1.730046
SEPCount_EFDATE_OCCLog 1.098293
AGELVL_B 1.163518
AGELVL_C 1.659501
AGELVL_E 1.404328
AGELVL_F 1.279390
AGELVL_G 1.266026
AGELVL_H 1.357187
AGELVL_I 1.513125
LOC_01 1.040148
LOC_02 1.023631
LOC_04 1.083542
LOC_06 1.161719
LOC_08 1.074488
LOC_13 1.089741
LOC_17 1.051699
LOC_18 1.022117
LOC_19 1.013164
LOC_20 1.020041
LOC_24 1.207112
LOC_25 1.031373
LOC_27 1.023343
LOC_29 1.043193
LOC_30 1.028860
LOC_32 1.023631
LOC_34 1.032500
LOC_35 1.066512
LOC_38 1.012487
LOC_39 1.056964
LOC_40 1.044558
LOC_42 1.056548
LOC_44 1.006347
LOC_46 1.028047
LOC_47 1.027067
LOC_48 1.159125
LOC_49 1.027123
LOC_50 1.004522
LOC_51 1.214843
LOC_53 1.075524
LOC_55 1.024151
TOA_15 1.402810
TOA_20 1.026875
TOA_30 1.211641
TOA_35 1.073533
TOA_38 1.158785
TOA_40 1.055889
TOA_42 1.009746
TOA_44 1.003025
PPGROUP_11 1.147630
Removed AFTER this step: TOA_40
Call:
glm(formula = fla, family = "binomial", data = OPMAnalysisDataNoFamBinary)
Deviance Residuals:
Min 1Q Median 3Q Max
-2.8205 -0.7409 -0.1383 0.8194 3.1997
Coefficients:
Estimate Std. Error z value Pr(>|z|)
(Intercept) 2.159341 0.332844 6.488 8.72e-11 ***
GSEGRD -0.065045 0.014394 -4.519 6.22e-06 ***
LowerLimitAge -0.044717 0.002617 -17.089 < 2e-16 ***
BLS_FEDERAL_OtherSep_Rate 3.295493 0.292825 11.254 < 2e-16 ***
BLS_FEDERAL_Quits_Rate 0.455954 0.312127 1.461 0.144072
BLS_FEDERAL_JobOpenings_Level 0.017217 0.001847 9.322 < 2e-16 ***
LOSSqrt -0.777807 0.021834 -35.623 < 2e-16 ***
SEPCount_EFDATE_OCCLog -0.032121 0.016017 -2.005 0.044916 *
AGELVL_B1 -0.579293 0.219524 -2.639 0.008319 **
AGELVL_C1 -0.167942 0.085266 -1.970 0.048882 *
AGELVL_E1 0.276813 0.067221 4.118 3.82e-05 ***
AGELVL_F1 0.492899 0.069984 7.043 1.88e-12 ***
AGELVL_G1 0.716316 0.073835 9.702 < 2e-16 ***
AGELVL_H1 0.848806 0.076113 11.152 < 2e-16 ***
AGELVL_I1 0.718648 0.080988 8.874 < 2e-16 ***
LOC_011 -0.215454 0.165564 -1.301 0.193144
LOC_021 0.155552 0.249304 0.624 0.532662
LOC_041 0.520412 0.134361 3.873 0.000107 ***
LOC_061 0.414320 0.084596 4.898 9.70e-07 ***
LOC_081 0.140973 0.123294 1.143 0.252877
LOC_131 0.164903 0.112961 1.460 0.144341
LOC_171 -0.223065 0.142328 -1.567 0.117053
LOC_181 -0.234853 0.249081 -0.943 0.345744
LOC_191 0.209713 0.317886 0.660 0.509439
LOC_201 0.607970 0.253788 2.396 0.016594 *
LOC_241 -0.112628 0.080334 -1.402 0.160916
LOC_251 0.144152 0.179525 0.803 0.421995
LOC_271 0.285111 0.235528 1.211 0.226079
LOC_291 -0.354741 0.166618 -2.129 0.033248 *
LOC_301 0.675529 0.237825 2.840 0.004505 **
LOC_321 0.226042 0.241701 0.935 0.349680
LOC_341 -0.179969 0.193374 -0.931 0.352021
LOC_351 0.465113 0.148379 3.135 0.001721 **
LOC_381 0.482802 0.364889 1.323 0.185786
LOC_391 -0.292731 0.140123 -2.089 0.036699 *
LOC_401 0.174402 0.163105 1.069 0.284950
LOC_421 -0.300973 0.141256 -2.131 0.033115 *
LOC_441 0.330442 0.491305 0.673 0.501214
LOC_461 0.809346 0.251594 3.217 0.001296 **
LOC_471 -0.186361 0.207532 -0.898 0.369194
LOC_481 0.248828 0.089543 2.779 0.005455 **
LOC_491 -0.189519 0.206412 -0.918 0.358534
LOC_501 -0.419348 0.591240 -0.709 0.478158
LOC_511 -0.155054 0.075652 -2.050 0.040407 *
LOC_531 0.396639 0.125811 3.153 0.001618 **
LOC_551 -0.324583 0.248334 -1.307 0.191198
TOA_151 0.071403 0.068487 1.043 0.297144
TOA_201 1.127198 0.192524 5.855 4.77e-09 ***
TOA_301 0.349539 0.087612 3.990 6.62e-05 ***
TOA_351 -0.536993 0.285998 -1.878 0.060434 .
TOA_381 0.334868 0.066521 5.034 4.80e-07 ***
TOA_421 1.150437 0.441050 2.608 0.009097 **
TOA_441 2.166692 1.071713 2.022 0.043207 *
PPGROUP_111 -0.405481 0.116632 -3.477 0.000508 ***
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
(Dispersion parameter for binomial family taken to be 1)
Null deviance: 20683 on 14919 degrees of freedom
Residual deviance: 14547 on 14866 degrees of freedom
AIC: 14655
Number of Fisher Scoring iterations: 5
VIF
GSEGRD 1.487185
LowerLimitAge 2.249881
BLS_FEDERAL_OtherSep_Rate 1.340745
BLS_FEDERAL_Quits_Rate 1.210656
BLS_FEDERAL_JobOpenings_Level 1.088865
LOSSqrt 1.689806
SEPCount_EFDATE_OCCLog 1.096528
AGELVL_B 1.163546
AGELVL_C 1.658576
AGELVL_E 1.403573
AGELVL_F 1.278909
AGELVL_G 1.265762
AGELVL_H 1.356983
AGELVL_I 1.513202
LOC_01 1.039832
LOC_02 1.023585
LOC_04 1.083224
LOC_06 1.161538
LOC_08 1.074263
LOC_13 1.089078
LOC_17 1.051427
LOC_18 1.022055
LOC_19 1.013121
LOC_20 1.020015
LOC_24 1.205349
LOC_25 1.031318
LOC_27 1.023271
LOC_29 1.043059
LOC_30 1.028833
LOC_32 1.023553
LOC_34 1.032307
LOC_35 1.066566
LOC_38 1.012456
LOC_39 1.056736
LOC_40 1.044162
LOC_42 1.056481
LOC_44 1.006012
LOC_46 1.027944
LOC_47 1.027047
LOC_48 1.158926
LOC_49 1.026938
LOC_50 1.004489
LOC_51 1.214545
LOC_53 1.075086
LOC_55 1.024033
TOA_15 1.380826
TOA_20 1.025897
TOA_30 1.204261
TOA_35 1.072641
TOA_38 1.150835
TOA_42 1.009426
TOA_44 1.002985
PPGROUP_11 1.147404
Removed AFTER this step: LOC_02
Call:
glm(formula = fla, family = "binomial", data = OPMAnalysisDataNoFamBinary)
Deviance Residuals:
Min 1Q Median 3Q Max
-2.8213 -0.7410 -0.1383 0.8191 3.1986
Coefficients:
Estimate Std. Error z value Pr(>|z|)
(Intercept) 2.169022 0.332474 6.524 6.85e-11 ***
GSEGRD -0.065595 0.014367 -4.566 4.98e-06 ***
LowerLimitAge -0.044709 0.002617 -17.087 < 2e-16 ***
BLS_FEDERAL_OtherSep_Rate 3.296321 0.292822 11.257 < 2e-16 ***
BLS_FEDERAL_Quits_Rate 0.456051 0.312130 1.461 0.14399
BLS_FEDERAL_JobOpenings_Level 0.017213 0.001847 9.320 < 2e-16 ***
LOSSqrt -0.777519 0.021827 -35.623 < 2e-16 ***
SEPCount_EFDATE_OCCLog -0.032154 0.016016 -2.008 0.04469 *
AGELVL_B1 -0.581429 0.219502 -2.649 0.00808 **
AGELVL_C1 -0.168697 0.085256 -1.979 0.04785 *
AGELVL_E1 0.276367 0.067215 4.112 3.93e-05 ***
AGELVL_F1 0.493483 0.069976 7.052 1.76e-12 ***
AGELVL_G1 0.716618 0.073835 9.706 < 2e-16 ***
AGELVL_H1 0.849681 0.076099 11.166 < 2e-16 ***
AGELVL_I1 0.718722 0.080987 8.875 < 2e-16 ***
LOC_011 -0.219095 0.165455 -1.324 0.18544
LOC_041 0.516227 0.134188 3.847 0.00012 ***
LOC_061 0.410572 0.084379 4.866 1.14e-06 ***
LOC_081 0.137027 0.123131 1.113 0.26577
LOC_131 0.161130 0.112796 1.429 0.15315
LOC_171 -0.226665 0.142201 -1.594 0.11094
LOC_181 -0.238346 0.249000 -0.957 0.33846
LOC_191 0.205952 0.317785 0.648 0.51693
LOC_201 0.603780 0.253686 2.380 0.01731 *
LOC_241 -0.115951 0.080157 -1.447 0.14802
LOC_251 0.140571 0.179421 0.783 0.43335
LOC_271 0.281081 0.235430 1.194 0.23251
LOC_291 -0.358617 0.166495 -2.154 0.03125 *
LOC_301 0.671016 0.237706 2.823 0.00476 **
LOC_321 0.222217 0.241610 0.920 0.35771
LOC_341 -0.183412 0.193286 -0.949 0.34266
LOC_351 0.460975 0.148224 3.110 0.00187 **
LOC_381 0.478444 0.364801 1.312 0.18968
LOC_391 -0.296333 0.139997 -2.117 0.03428 *
LOC_401 0.170449 0.162973 1.046 0.29562
LOC_421 -0.304371 0.141143 -2.156 0.03105 *
LOC_441 0.326404 0.491193 0.665 0.50636
LOC_461 0.804818 0.251475 3.200 0.00137 **
LOC_471 -0.190120 0.207431 -0.917 0.35938
LOC_481 0.244937 0.089323 2.742 0.00610 **
LOC_491 -0.193408 0.206310 -0.937 0.34852
LOC_501 -0.422945 0.591177 -0.715 0.47434
LOC_511 -0.158691 0.075431 -2.104 0.03540 *
LOC_531 0.392713 0.125648 3.126 0.00177 **
LOC_551 -0.328164 0.248245 -1.322 0.18619
TOA_151 0.071846 0.068479 1.049 0.29410
TOA_201 1.128002 0.192480 5.860 4.62e-09 ***
TOA_301 0.348580 0.087595 3.979 6.91e-05 ***
TOA_351 -0.539216 0.285987 -1.885 0.05937 .
TOA_381 0.333774 0.066494 5.020 5.18e-07 ***
TOA_421 1.149154 0.441057 2.605 0.00918 **
TOA_441 2.164204 1.071701 2.019 0.04344 *
PPGROUP_111 -0.405948 0.116626 -3.481 0.00050 ***
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
(Dispersion parameter for binomial family taken to be 1)
Null deviance: 20683 on 14919 degrees of freedom
Residual deviance: 14547 on 14867 degrees of freedom
AIC: 14653
Number of Fisher Scoring iterations: 5
VIF
GSEGRD 1.481654
LowerLimitAge 2.249661
BLS_FEDERAL_OtherSep_Rate 1.340748
BLS_FEDERAL_Quits_Rate 1.210664
BLS_FEDERAL_JobOpenings_Level 1.088837
LOSSqrt 1.688752
SEPCount_EFDATE_OCCLog 1.096531
AGELVL_B 1.163278
AGELVL_C 1.658219
AGELVL_E 1.403404
AGELVL_F 1.278689
AGELVL_G 1.265681
AGELVL_H 1.356492
AGELVL_I 1.513072
LOC_01 1.038549
LOC_04 1.080522
LOC_06 1.155708
LOC_08 1.071440
LOC_13 1.085967
LOC_17 1.049719
LOC_18 1.021546
LOC_19 1.012762
LOC_20 1.019301
LOC_24 1.200105
LOC_25 1.030274
LOC_27 1.022506
LOC_29 1.041619
LOC_30 1.027878
LOC_32 1.022898
LOC_34 1.031483
LOC_35 1.064436
LOC_38 1.012088
LOC_39 1.054960
LOC_40 1.042594
LOC_42 1.054934
LOC_44 1.005838
LOC_46 1.027085
LOC_47 1.026188
LOC_48 1.153324
LOC_49 1.026005
LOC_50 1.004394
LOC_51 1.207361
LOC_53 1.072411
LOC_55 1.023498
TOA_15 1.380722
TOA_20 1.025931
TOA_30 1.203909
TOA_35 1.072500
TOA_38 1.150034
TOA_42 1.009406
TOA_44 1.002972
PPGROUP_11 1.147358
Removed AFTER this step: LOC_19
Call:
glm(formula = fla, family = "binomial", data = OPMAnalysisDataNoFamBinary)
Deviance Residuals:
Min 1Q Median 3Q Max
-2.8214 -0.7410 -0.1383 0.8191 3.1978
Coefficients:
Estimate Std. Error z value Pr(>|z|)
(Intercept) 2.173687 0.332390 6.540 6.17e-11 ***
GSEGRD -0.065812 0.014364 -4.582 4.61e-06 ***
LowerLimitAge -0.044688 0.002616 -17.080 < 2e-16 ***
BLS_FEDERAL_OtherSep_Rate 3.295948 0.292824 11.256 < 2e-16 ***
BLS_FEDERAL_Quits_Rate 0.455946 0.312132 1.461 0.144084
BLS_FEDERAL_JobOpenings_Level 0.017221 0.001847 9.324 < 2e-16 ***
LOSSqrt -0.777456 0.021825 -35.622 < 2e-16 ***
SEPCount_EFDATE_OCCLog -0.032370 0.016013 -2.021 0.043232 *
AGELVL_B1 -0.580426 0.219521 -2.644 0.008192 **
AGELVL_C1 -0.168460 0.085252 -1.976 0.048152 *
AGELVL_E1 0.276674 0.067215 4.116 3.85e-05 ***
AGELVL_F1 0.493645 0.069977 7.054 1.73e-12 ***
AGELVL_G1 0.716168 0.073830 9.700 < 2e-16 ***
AGELVL_H1 0.849582 0.076095 11.165 < 2e-16 ***
AGELVL_I1 0.718710 0.080984 8.875 < 2e-16 ***
LOC_011 -0.222029 0.165395 -1.342 0.179461
LOC_041 0.513322 0.134108 3.828 0.000129 ***
LOC_061 0.407596 0.084253 4.838 1.31e-06 ***
LOC_081 0.134257 0.123056 1.091 0.275263
LOC_131 0.158342 0.112713 1.405 0.160075
LOC_171 -0.229485 0.142134 -1.615 0.106404
LOC_181 -0.241620 0.248948 -0.971 0.331764
LOC_201 0.600885 0.253637 2.369 0.017833 *
LOC_241 -0.118521 0.080059 -1.480 0.138761
LOC_251 0.137740 0.179366 0.768 0.442531
LOC_271 0.277864 0.235383 1.180 0.237809
LOC_291 -0.361699 0.166428 -2.173 0.029757 *
LOC_301 0.667897 0.237648 2.810 0.004947 **
LOC_321 0.218889 0.241556 0.906 0.364851
LOC_341 -0.186328 0.193237 -0.964 0.334922
LOC_351 0.458155 0.148157 3.092 0.001986 **
LOC_381 0.475284 0.364750 1.303 0.192561
LOC_391 -0.299316 0.139923 -2.139 0.032423 *
LOC_401 0.167599 0.162907 1.029 0.303573
LOC_421 -0.307211 0.141075 -2.178 0.029433 *
LOC_441 0.323275 0.491151 0.658 0.510411
LOC_461 0.801741 0.251422 3.189 0.001429 **
LOC_471 -0.193225 0.207377 -0.932 0.351463
LOC_481 0.242071 0.089212 2.713 0.006659 **
LOC_491 -0.196442 0.206255 -0.952 0.340882
LOC_501 -0.426012 0.591167 -0.721 0.471137
LOC_511 -0.161377 0.075319 -2.143 0.032147 *
LOC_531 0.389767 0.125562 3.104 0.001908 **
LOC_551 -0.331540 0.248193 -1.336 0.181609
TOA_151 0.071387 0.068477 1.043 0.297180
TOA_201 1.127031 0.192477 5.855 4.76e-09 ***
TOA_301 0.347554 0.087580 3.968 7.23e-05 ***
TOA_351 -0.541090 0.285979 -1.892 0.058483 .
TOA_381 0.334941 0.066461 5.040 4.66e-07 ***
TOA_421 1.147843 0.441061 2.602 0.009256 **
TOA_441 2.161923 1.071684 2.017 0.043663 *
PPGROUP_111 -0.405656 0.116620 -3.478 0.000504 ***
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
(Dispersion parameter for binomial family taken to be 1)
Null deviance: 20683 on 14919 degrees of freedom
Residual deviance: 14548 on 14868 degrees of freedom
AIC: 14652
Number of Fisher Scoring iterations: 5
VIF
GSEGRD 1.480809
LowerLimitAge 2.249496
BLS_FEDERAL_OtherSep_Rate 1.340705
BLS_FEDERAL_Quits_Rate 1.210674
BLS_FEDERAL_JobOpenings_Level 1.088762
LOSSqrt 1.688682
SEPCount_EFDATE_OCCLog 1.096064
AGELVL_B 1.163200
AGELVL_C 1.658214
AGELVL_E 1.403332
AGELVL_F 1.278691
AGELVL_G 1.265575
AGELVL_H 1.356512
AGELVL_I 1.513122
LOC_01 1.037773
LOC_04 1.079344
LOC_06 1.152315
LOC_08 1.070173
LOC_13 1.084412
LOC_17 1.048750
LOC_18 1.021126
LOC_20 1.018992
LOC_24 1.197217
LOC_25 1.029670
LOC_27 1.022055
LOC_29 1.040782
LOC_30 1.027463
LOC_32 1.022438
LOC_34 1.030931
LOC_35 1.063537
LOC_38 1.011912
LOC_39 1.053827
LOC_40 1.041853
LOC_42 1.053930
LOC_44 1.005741
LOC_46 1.026727
LOC_47 1.025646
LOC_48 1.150540
LOC_49 1.025485
LOC_50 1.004330
LOC_51 1.203790
LOC_53 1.071031
LOC_55 1.023050
TOA_15 1.380558
TOA_20 1.025870
TOA_30 1.203504
TOA_35 1.072384
TOA_38 1.149319
TOA_42 1.009386
TOA_44 1.002961
PPGROUP_11 1.147375
Removed AFTER this step: LOC_44
Call:
glm(formula = fla, family = "binomial", data = OPMAnalysisDataNoFamBinary)
Deviance Residuals:
Min 1Q Median 3Q Max
-2.8213 -0.7408 -0.1384 0.8193 3.1969
Coefficients:
Estimate Std. Error z value Pr(>|z|)
(Intercept) 2.178033 0.332322 6.554 5.60e-11 ***
GSEGRD -0.065972 0.014362 -4.594 4.36e-06 ***
LowerLimitAge -0.044655 0.002616 -17.071 < 2e-16 ***
BLS_FEDERAL_OtherSep_Rate 3.293772 0.292810 11.249 < 2e-16 ***
BLS_FEDERAL_Quits_Rate 0.457459 0.312119 1.466 0.142742
BLS_FEDERAL_JobOpenings_Level 0.017199 0.001847 9.314 < 2e-16 ***
LOSSqrt -0.777372 0.021824 -35.620 < 2e-16 ***
SEPCount_EFDATE_OCCLog -0.032424 0.016013 -2.025 0.042881 *
AGELVL_B1 -0.579635 0.219491 -2.641 0.008271 **
AGELVL_C1 -0.168665 0.085250 -1.978 0.047875 *
AGELVL_E1 0.276445 0.067211 4.113 3.90e-05 ***
AGELVL_F1 0.493083 0.069971 7.047 1.83e-12 ***
AGELVL_G1 0.715405 0.073822 9.691 < 2e-16 ***
AGELVL_H1 0.849099 0.076088 11.159 < 2e-16 ***
AGELVL_I1 0.718276 0.080983 8.870 < 2e-16 ***
LOC_011 -0.223879 0.165362 -1.354 0.175777
LOC_041 0.511302 0.134064 3.814 0.000137 ***
LOC_061 0.405709 0.084199 4.818 1.45e-06 ***
LOC_081 0.132402 0.123017 1.076 0.281797
LOC_131 0.156525 0.112672 1.389 0.164770
LOC_171 -0.231296 0.142100 -1.628 0.103588
LOC_181 -0.243457 0.248909 -0.978 0.328026
LOC_201 0.598891 0.253600 2.362 0.018199 *
LOC_241 -0.120178 0.080015 -1.502 0.133113
LOC_251 0.135983 0.179334 0.758 0.448292
LOC_271 0.275992 0.235356 1.173 0.240934
LOC_291 -0.363600 0.166395 -2.185 0.028877 *
LOC_301 0.665680 0.237608 2.802 0.005085 **
LOC_321 0.216920 0.241521 0.898 0.369109
LOC_341 -0.188040 0.193211 -0.973 0.330436
LOC_351 0.456147 0.148115 3.080 0.002072 **
LOC_381 0.473223 0.364705 1.298 0.194442
LOC_391 -0.301150 0.139887 -2.153 0.031334 *
LOC_401 0.165749 0.162871 1.018 0.308835
LOC_421 -0.308964 0.141043 -2.191 0.028483 *
LOC_461 0.799585 0.251380 3.181 0.001469 **
LOC_471 -0.195177 0.207343 -0.941 0.346539
LOC_481 0.240169 0.089160 2.694 0.007067 **
LOC_491 -0.198293 0.206220 -0.962 0.336269
LOC_501 -0.427852 0.591105 -0.724 0.469177
LOC_511 -0.163117 0.075271 -2.167 0.030230 *
LOC_531 0.387884 0.125521 3.090 0.002000 **
LOC_551 -0.333439 0.248162 -1.344 0.179066
TOA_151 0.071191 0.068474 1.040 0.298486
TOA_201 1.126254 0.192464 5.852 4.86e-09 ***
TOA_301 0.347180 0.087569 3.965 7.35e-05 ***
TOA_351 -0.542215 0.285973 -1.896 0.057956 .
TOA_381 0.334720 0.066458 5.037 4.74e-07 ***
TOA_421 1.147075 0.441055 2.601 0.009302 **
TOA_441 2.160663 1.071681 2.016 0.043785 *
PPGROUP_111 -0.405472 0.116610 -3.477 0.000507 ***
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
(Dispersion parameter for binomial family taken to be 1)
Null deviance: 20683 on 14919 degrees of freedom
Residual deviance: 14548 on 14869 degrees of freedom
AIC: 14650
Number of Fisher Scoring iterations: 5
VIF
GSEGRD 1.480452
LowerLimitAge 2.248635
BLS_FEDERAL_OtherSep_Rate 1.340564
BLS_FEDERAL_Quits_Rate 1.210606
BLS_FEDERAL_JobOpenings_Level 1.088423
LOSSqrt 1.688586
SEPCount_EFDATE_OCCLog 1.096081
AGELVL_B 1.163230
AGELVL_C 1.658207
AGELVL_E 1.403348
AGELVL_F 1.278508
AGELVL_G 1.265252
AGELVL_H 1.356430
AGELVL_I 1.513087
LOC_01 1.037476
LOC_04 1.078792
LOC_06 1.151009
LOC_08 1.069624
LOC_13 1.083775
LOC_17 1.048364
LOC_18 1.021003
LOC_20 1.018850
LOC_24 1.196049
LOC_25 1.029446
LOC_27 1.021906
LOC_29 1.040475
LOC_30 1.027260
LOC_32 1.022286
LOC_34 1.030748
LOC_35 1.063099
LOC_38 1.011841
LOC_39 1.053416
LOC_40 1.041553
LOC_42 1.053561
LOC_46 1.026558
LOC_47 1.025440
LOC_48 1.149361
LOC_49 1.025299
LOC_50 1.004308
LOC_51 1.202324
LOC_53 1.070487
LOC_55 1.022915
TOA_15 1.380541
TOA_20 1.025829
TOA_30 1.203452
TOA_35 1.072314
TOA_38 1.149291
TOA_42 1.009379
TOA_44 1.002957
PPGROUP_11 1.147387
Removed AFTER this step: LOC_50
Call:
glm(formula = fla, family = "binomial", data = OPMAnalysisDataNoFamBinary)
Deviance Residuals:
Min 1Q Median 3Q Max
-2.8216 -0.7414 -0.1384 0.8195 3.1974
Coefficients:
Estimate Std. Error z value Pr(>|z|)
(Intercept) 2.178398 0.332333 6.555 5.57e-11 ***
GSEGRD -0.066011 0.014361 -4.596 4.30e-06 ***
LowerLimitAge -0.044682 0.002616 -17.083 < 2e-16 ***
BLS_FEDERAL_OtherSep_Rate 3.292869 0.292809 11.246 < 2e-16 ***
BLS_FEDERAL_Quits_Rate 0.457110 0.312100 1.465 0.143023
BLS_FEDERAL_JobOpenings_Level 0.017207 0.001847 9.318 < 2e-16 ***
LOSSqrt -0.777182 0.021822 -35.614 < 2e-16 ***
SEPCount_EFDATE_OCCLog -0.032426 0.016012 -2.025 0.042851 *
AGELVL_B1 -0.579526 0.219486 -2.640 0.008281 **
AGELVL_C1 -0.168881 0.085245 -1.981 0.047578 *
AGELVL_E1 0.276819 0.067207 4.119 3.81e-05 ***
AGELVL_F1 0.492897 0.069969 7.044 1.86e-12 ***
AGELVL_G1 0.714823 0.073810 9.685 < 2e-16 ***
AGELVL_H1 0.849528 0.076087 11.165 < 2e-16 ***
AGELVL_I1 0.717806 0.080980 8.864 < 2e-16 ***
LOC_011 -0.222196 0.165345 -1.344 0.179002
LOC_041 0.513048 0.134042 3.828 0.000129 ***
LOC_061 0.407393 0.084166 4.840 1.30e-06 ***
LOC_081 0.134063 0.122995 1.090 0.275718
LOC_131 0.158164 0.112648 1.404 0.160305
LOC_171 -0.229705 0.142081 -1.617 0.105939
LOC_181 -0.241574 0.248890 -0.971 0.331744
LOC_201 0.600682 0.253582 2.369 0.017847 *
LOC_241 -0.118571 0.079984 -1.482 0.138225
LOC_251 0.137579 0.179321 0.767 0.442949
LOC_271 0.277685 0.235339 1.180 0.238026
LOC_291 -0.361929 0.166375 -2.175 0.029601 *
LOC_301 0.667413 0.237600 2.809 0.004970 **
LOC_321 0.218861 0.241502 0.906 0.364805
LOC_341 -0.186425 0.193195 -0.965 0.334565
LOC_351 0.457813 0.148098 3.091 0.001993 **
LOC_381 0.474986 0.364692 1.302 0.192770
LOC_391 -0.299449 0.139866 -2.141 0.032276 *
LOC_401 0.167363 0.162855 1.028 0.304100
LOC_421 -0.307332 0.141023 -2.179 0.029309 *
LOC_461 0.801261 0.251367 3.188 0.001435 **
LOC_471 -0.193380 0.207325 -0.933 0.350954
LOC_481 0.241900 0.089127 2.714 0.006646 **
LOC_491 -0.196560 0.206203 -0.953 0.340469
LOC_511 -0.161523 0.075237 -2.147 0.031805 *
LOC_531 0.389511 0.125501 3.104 0.001912 **
LOC_551 -0.331644 0.248141 -1.337 0.181382
TOA_151 0.070978 0.068466 1.037 0.299876
TOA_201 1.126715 0.192464 5.854 4.79e-09 ***
TOA_301 0.348077 0.087561 3.975 7.03e-05 ***
TOA_351 -0.541841 0.285966 -1.895 0.058122 .
TOA_381 0.333947 0.066446 5.026 5.01e-07 ***
TOA_421 1.147696 0.441056 2.602 0.009264 **
TOA_441 2.162176 1.071672 2.018 0.043636 *
PPGROUP_111 -0.406315 0.116605 -3.485 0.000493 ***
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
(Dispersion parameter for binomial family taken to be 1)
Null deviance: 20683 on 14919 degrees of freedom
Residual deviance: 14549 on 14870 degrees of freedom
AIC: 14649
Number of Fisher Scoring iterations: 5
VIF
GSEGRD 1.480438
LowerLimitAge 2.248104
BLS_FEDERAL_OtherSep_Rate 1.340503
BLS_FEDERAL_Quits_Rate 1.210604
BLS_FEDERAL_JobOpenings_Level 1.088412
LOSSqrt 1.688238
SEPCount_EFDATE_OCCLog 1.096052
AGELVL_B 1.163210
AGELVL_C 1.658174
AGELVL_E 1.403241
AGELVL_F 1.278470
AGELVL_G 1.265036
AGELVL_H 1.356328
AGELVL_I 1.512796
LOC_01 1.037272
LOC_04 1.078435
LOC_06 1.150122
LOC_08 1.069244
LOC_13 1.083333
LOC_17 1.048109
LOC_18 1.020890
LOC_20 1.018751
LOC_24 1.195107
LOC_25 1.029288
LOC_27 1.021805
LOC_29 1.040272
LOC_30 1.027153
LOC_32 1.022159
LOC_34 1.030606
LOC_35 1.062835
LOC_38 1.011793
LOC_39 1.053115
LOC_40 1.041352
LOC_42 1.053284
LOC_46 1.026469
LOC_47 1.025291
LOC_48 1.148528
LOC_49 1.025159
LOC_51 1.201261
LOC_53 1.070134
LOC_55 1.022810
TOA_15 1.380607
TOA_20 1.025817
TOA_30 1.203214
TOA_35 1.072308
TOA_38 1.148895
TOA_42 1.009373
TOA_44 1.002954
PPGROUP_11 1.147269
Removed AFTER this step: LOC_25
Call:
glm(formula = fla, family = "binomial", data = OPMAnalysisDataNoFamBinary)
Deviance Residuals:
Min 1Q Median 3Q Max
-2.8225 -0.7409 -0.1384 0.8199 3.1958
Coefficients:
Estimate Std. Error z value Pr(>|z|)
(Intercept) 2.185356 0.332210 6.578 4.76e-11 ***
GSEGRD -0.066215 0.014358 -4.612 4.00e-06 ***
LowerLimitAge -0.044693 0.002616 -17.087 < 2e-16 ***
BLS_FEDERAL_OtherSep_Rate 3.296389 0.292766 11.259 < 2e-16 ***
BLS_FEDERAL_Quits_Rate 0.454744 0.312089 1.457 0.145089
BLS_FEDERAL_JobOpenings_Level 0.017214 0.001846 9.323 < 2e-16 ***
LOSSqrt -0.777074 0.021821 -35.611 < 2e-16 ***
SEPCount_EFDATE_OCCLog -0.032425 0.016012 -2.025 0.042868 *
AGELVL_B1 -0.577086 0.219416 -2.630 0.008536 **
AGELVL_C1 -0.168796 0.085240 -1.980 0.047677 *
AGELVL_E1 0.276771 0.067205 4.118 3.82e-05 ***
AGELVL_F1 0.493251 0.069963 7.050 1.79e-12 ***
AGELVL_G1 0.714651 0.073806 9.683 < 2e-16 ***
AGELVL_H1 0.849741 0.076088 11.168 < 2e-16 ***
AGELVL_I1 0.718239 0.080984 8.869 < 2e-16 ***
LOC_011 -0.227884 0.165181 -1.380 0.167710
LOC_041 0.507415 0.133842 3.791 0.000150 ***
LOC_061 0.401730 0.083845 4.791 1.66e-06 ***
LOC_081 0.128453 0.122781 1.046 0.295471
LOC_131 0.152561 0.112415 1.357 0.174744
LOC_171 -0.235348 0.141892 -1.659 0.097189 .
LOC_181 -0.247271 0.248785 -0.994 0.320263
LOC_201 0.595057 0.253471 2.348 0.018893 *
LOC_241 -0.123962 0.079679 -1.556 0.119764
LOC_271 0.271817 0.235219 1.156 0.247850
LOC_291 -0.367680 0.166211 -2.212 0.026958 *
LOC_301 0.661652 0.237479 2.786 0.005334 **
LOC_321 0.213144 0.241394 0.883 0.377252
LOC_341 -0.192148 0.193057 -0.995 0.319595
LOC_351 0.452144 0.147914 3.057 0.002237 **
LOC_381 0.469317 0.364615 1.287 0.198039
LOC_391 -0.305098 0.139676 -2.184 0.028938 *
LOC_401 0.161686 0.162687 0.994 0.320297
LOC_421 -0.313009 0.140831 -2.223 0.026244 *
LOC_461 0.795483 0.251254 3.166 0.001545 **
LOC_471 -0.199060 0.207195 -0.961 0.336685
LOC_481 0.236310 0.088831 2.660 0.007809 **
LOC_491 -0.202279 0.206072 -0.982 0.326300
LOC_511 -0.167083 0.074893 -2.231 0.025684 *
LOC_531 0.383776 0.125281 3.063 0.002189 **
LOC_551 -0.337397 0.248035 -1.360 0.173742
TOA_151 0.070610 0.068463 1.031 0.302377
TOA_201 1.128703 0.192431 5.865 4.48e-09 ***
TOA_301 0.348165 0.087557 3.976 7.00e-05 ***
TOA_351 -0.544112 0.285939 -1.903 0.057054 .
TOA_381 0.334081 0.066438 5.028 4.94e-07 ***
TOA_421 1.145523 0.441063 2.597 0.009399 **
TOA_441 2.157064 1.071651 2.013 0.044131 *
PPGROUP_111 -0.406077 0.116607 -3.482 0.000497 ***
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
(Dispersion parameter for binomial family taken to be 1)
Null deviance: 20683 on 14919 degrees of freedom
Residual deviance: 14549 on 14871 degrees of freedom
AIC: 14647
Number of Fisher Scoring iterations: 5
VIF
GSEGRD 1.479926
LowerLimitAge 2.248229
BLS_FEDERAL_OtherSep_Rate 1.340278
BLS_FEDERAL_Quits_Rate 1.210517
BLS_FEDERAL_JobOpenings_Level 1.088348
LOSSqrt 1.688115
SEPCount_EFDATE_OCCLog 1.096060
AGELVL_B 1.162989
AGELVL_C 1.658226
AGELVL_E 1.403223
AGELVL_F 1.278456
AGELVL_G 1.265052
AGELVL_H 1.356344
AGELVL_I 1.512637
LOC_01 1.035198
LOC_04 1.075230
LOC_06 1.141334
LOC_08 1.065492
LOC_13 1.078816
LOC_17 1.045329
LOC_18 1.019989
LOC_20 1.017908
LOC_24 1.185992
LOC_27 1.020732
LOC_29 1.038175
LOC_30 1.026131
LOC_32 1.021193
LOC_34 1.029083
LOC_35 1.060207
LOC_38 1.011383
LOC_39 1.050218
LOC_40 1.039225
LOC_42 1.050413
LOC_46 1.025551
LOC_47 1.023994
LOC_48 1.140914
LOC_49 1.023830
LOC_51 1.190253
LOC_53 1.066363
LOC_55 1.021883
TOA_15 1.380547
TOA_20 1.025636
TOA_30 1.203224
TOA_35 1.072257
TOA_38 1.148892
TOA_42 1.009336
TOA_44 1.002915
PPGROUP_11 1.147275
Removed AFTER this step: LOC_32
Call:
glm(formula = fla, family = "binomial", data = OPMAnalysisDataNoFamBinary)
Deviance Residuals:
Min 1Q Median 3Q Max
-2.8245 -0.7411 -0.1384 0.8200 3.1946
Coefficients:
Estimate Std. Error z value Pr(>|z|)
(Intercept) 2.194898 0.332025 6.611 3.83e-11 ***
GSEGRD -0.066559 0.014353 -4.637 3.53e-06 ***
LowerLimitAge -0.044646 0.002615 -17.074 < 2e-16 ***
BLS_FEDERAL_OtherSep_Rate 3.294422 0.292759 11.253 < 2e-16 ***
BLS_FEDERAL_Quits_Rate 0.455059 0.312076 1.458 0.144794
BLS_FEDERAL_JobOpenings_Level 0.017207 0.001846 9.319 < 2e-16 ***
LOSSqrt -0.777315 0.021818 -35.627 < 2e-16 ***
SEPCount_EFDATE_OCCLog -0.032822 0.016006 -2.051 0.040311 *
AGELVL_B1 -0.579409 0.219416 -2.641 0.008274 **
AGELVL_C1 -0.169572 0.085235 -1.989 0.046649 *
AGELVL_E1 0.275971 0.067200 4.107 4.01e-05 ***
AGELVL_F1 0.493541 0.069952 7.055 1.72e-12 ***
AGELVL_G1 0.714063 0.073809 9.674 < 2e-16 ***
AGELVL_H1 0.849351 0.076082 11.164 < 2e-16 ***
AGELVL_I1 0.717852 0.080973 8.865 < 2e-16 ***
LOC_011 -0.232622 0.165105 -1.409 0.158855
LOC_041 0.502422 0.133723 3.757 0.000172 ***
LOC_061 0.396723 0.083658 4.742 2.11e-06 ***
LOC_081 0.123703 0.122669 1.008 0.313248
LOC_131 0.147944 0.112299 1.317 0.187701
LOC_171 -0.240024 0.141802 -1.693 0.090519 .
LOC_181 -0.252851 0.248721 -1.017 0.309342
LOC_201 0.590109 0.253422 2.329 0.019882 *
LOC_241 -0.128216 0.079537 -1.612 0.106955
LOC_271 0.266441 0.235166 1.133 0.257219
LOC_291 -0.372864 0.166125 -2.244 0.024802 *
LOC_301 0.656398 0.237414 2.765 0.005696 **
LOC_341 -0.196806 0.192993 -1.020 0.307844
LOC_351 0.447424 0.147818 3.027 0.002471 **
LOC_381 0.464045 0.364566 1.273 0.203064
LOC_391 -0.310056 0.139572 -2.221 0.026319 *
LOC_401 0.157015 0.162602 0.966 0.334222
LOC_421 -0.317628 0.140742 -2.257 0.024020 *
LOC_461 0.790372 0.251192 3.146 0.001652 **
LOC_471 -0.204409 0.207127 -0.987 0.323702
LOC_481 0.231477 0.088665 2.611 0.009036 **
LOC_491 -0.207352 0.206009 -1.007 0.314166
LOC_511 -0.171539 0.074731 -2.295 0.021709 *
LOC_531 0.379017 0.125164 3.028 0.002460 **
LOC_551 -0.343258 0.247968 -1.384 0.166273
TOA_151 0.070483 0.068464 1.029 0.303253
TOA_201 1.131682 0.192515 5.878 4.14e-09 ***
TOA_301 0.347246 0.087547 3.966 7.30e-05 ***
TOA_351 -0.546459 0.285947 -1.911 0.055998 .
TOA_381 0.336618 0.066371 5.072 3.94e-07 ***
TOA_421 1.143223 0.441064 2.592 0.009543 **
TOA_441 2.153369 1.071639 2.009 0.044493 *
PPGROUP_111 -0.404996 0.116596 -3.473 0.000514 ***
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
(Dispersion parameter for binomial family taken to be 1)
Null deviance: 20683 on 14919 degrees of freedom
Residual deviance: 14550 on 14872 degrees of freedom
AIC: 14646
Number of Fisher Scoring iterations: 5
VIF
GSEGRD 1.478702
LowerLimitAge 2.247078
BLS_FEDERAL_OtherSep_Rate 1.340209
BLS_FEDERAL_Quits_Rate 1.210590
BLS_FEDERAL_JobOpenings_Level 1.088248
LOSSqrt 1.687936
SEPCount_EFDATE_OCCLog 1.095150
AGELVL_B 1.162859
AGELVL_C 1.657980
AGELVL_E 1.402918
AGELVL_F 1.278470
AGELVL_G 1.264804
AGELVL_H 1.356124
AGELVL_I 1.512502
LOC_01 1.034110
LOC_04 1.073342
LOC_06 1.136166
LOC_08 1.063477
LOC_13 1.076514
LOC_17 1.043897
LOC_18 1.019338
LOC_20 1.017415
LOC_24 1.181750
LOC_27 1.020054
LOC_29 1.036893
LOC_30 1.025489
LOC_34 1.028337
LOC_35 1.058841
LOC_38 1.011119
LOC_39 1.048542
LOC_40 1.038150
LOC_42 1.048995
LOC_46 1.025012
LOC_47 1.023128
LOC_48 1.136639
LOC_49 1.023045
LOC_51 1.184976
LOC_53 1.064429
LOC_55 1.021160
TOA_15 1.380415
TOA_20 1.025419
TOA_30 1.203014
TOA_35 1.072185
TOA_38 1.146874
TOA_42 1.009303
TOA_44 1.002899
PPGROUP_11 1.147169
Removed AFTER this step: LOC_40
Call:
glm(formula = fla, family = "binomial", data = OPMAnalysisDataNoFamBinary)
Deviance Residuals:
Min 1Q Median 3Q Max
-2.8241 -0.7412 -0.1384 0.8202 3.1926
Coefficients:
Estimate Std. Error z value Pr(>|z|)
(Intercept) 2.212758 0.331501 6.675 2.47e-11 ***
GSEGRD -0.067298 0.014331 -4.696 2.65e-06 ***
LowerLimitAge -0.044626 0.002615 -17.068 < 2e-16 ***
BLS_FEDERAL_OtherSep_Rate 3.294398 0.292735 11.254 < 2e-16 ***
BLS_FEDERAL_Quits_Rate 0.454056 0.312076 1.455 0.145683
BLS_FEDERAL_JobOpenings_Level 0.017205 0.001846 9.319 < 2e-16 ***
LOSSqrt -0.777262 0.021819 -35.623 < 2e-16 ***
SEPCount_EFDATE_OCCLog -0.032525 0.016002 -2.033 0.042100 *
AGELVL_B1 -0.581892 0.219396 -2.652 0.007996 **
AGELVL_C1 -0.168129 0.085219 -1.973 0.048507 *
AGELVL_E1 0.276947 0.067196 4.122 3.76e-05 ***
AGELVL_F1 0.494631 0.069935 7.073 1.52e-12 ***
AGELVL_G1 0.713681 0.073804 9.670 < 2e-16 ***
AGELVL_H1 0.850017 0.076076 11.173 < 2e-16 ***
AGELVL_I1 0.718792 0.080967 8.878 < 2e-16 ***
LOC_011 -0.240166 0.164931 -1.456 0.145348
LOC_041 0.493798 0.133428 3.701 0.000215 ***
LOC_061 0.389110 0.083291 4.672 2.99e-06 ***
LOC_081 0.115838 0.122411 0.946 0.343994
LOC_131 0.140046 0.112006 1.250 0.211173
LOC_171 -0.247612 0.141587 -1.749 0.080320 .
LOC_181 -0.260299 0.248612 -1.047 0.295095
LOC_201 0.581818 0.253290 2.297 0.021617 *
LOC_241 -0.135643 0.079171 -1.713 0.086657 .
LOC_271 0.258507 0.235027 1.100 0.271375
LOC_291 -0.380790 0.165928 -2.295 0.021738 *
LOC_301 0.647695 0.237263 2.730 0.006336 **
LOC_341 -0.204434 0.192840 -1.060 0.289091
LOC_351 0.438787 0.147560 2.974 0.002943 **
LOC_381 0.455605 0.364497 1.250 0.211316
LOC_391 -0.317642 0.139356 -2.279 0.022645 *
LOC_421 -0.325120 0.140531 -2.314 0.020694 *
LOC_461 0.781369 0.251032 3.113 0.001854 **
LOC_471 -0.212136 0.206966 -1.025 0.305373
LOC_481 0.223435 0.088279 2.531 0.011374 *
LOC_491 -0.215332 0.205853 -1.046 0.295539
LOC_511 -0.179331 0.074307 -2.413 0.015806 *
LOC_531 0.370934 0.124890 2.970 0.002977 **
LOC_551 -0.350540 0.247847 -1.414 0.157262
TOA_151 0.070652 0.068462 1.032 0.302079
TOA_201 1.129217 0.192541 5.865 4.50e-09 ***
TOA_301 0.345442 0.087499 3.948 7.88e-05 ***
TOA_351 -0.548742 0.285874 -1.920 0.054918 .
TOA_381 0.334593 0.066335 5.044 4.56e-07 ***
TOA_421 1.140319 0.441075 2.585 0.009729 **
TOA_441 2.147433 1.071610 2.004 0.045077 *
PPGROUP_111 -0.408296 0.116506 -3.504 0.000457 ***
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
(Dispersion parameter for binomial family taken to be 1)
Null deviance: 20683 on 14919 degrees of freedom
Residual deviance: 14551 on 14873 degrees of freedom
AIC: 14645
Number of Fisher Scoring iterations: 5
VIF
GSEGRD 1.474094
LowerLimitAge 2.246894
BLS_FEDERAL_OtherSep_Rate 1.340107
BLS_FEDERAL_Quits_Rate 1.210536
BLS_FEDERAL_JobOpenings_Level 1.088166
LOSSqrt 1.688079
SEPCount_EFDATE_OCCLog 1.094754
AGELVL_B 1.162661
AGELVL_C 1.657351
AGELVL_E 1.402507
AGELVL_F 1.278284
AGELVL_G 1.264822
AGELVL_H 1.356156
AGELVL_I 1.512463
LOC_01 1.031813
LOC_04 1.068566
LOC_06 1.126183
LOC_08 1.058826
LOC_13 1.070850
LOC_17 1.040736
LOC_18 1.018376
LOC_20 1.016253
LOC_24 1.170786
LOC_27 1.018829
LOC_29 1.034399
LOC_30 1.024005
LOC_34 1.026644
LOC_35 1.054978
LOC_38 1.010541
LOC_39 1.045264
LOC_42 1.045865
LOC_46 1.023603
LOC_47 1.021623
LOC_48 1.126688
LOC_49 1.021417
LOC_51 1.171317
LOC_53 1.059714
LOC_55 1.020241
TOA_15 1.380371
TOA_20 1.025235
TOA_30 1.202260
TOA_35 1.072083
TOA_38 1.145634
TOA_42 1.009265
TOA_44 1.002866
PPGROUP_11 1.146138
Removed AFTER this step: LOC_08
Call:
glm(formula = fla, family = "binomial", data = OPMAnalysisDataNoFamBinary)
Deviance Residuals:
Min 1Q Median 3Q Max
-2.8251 -0.7410 -0.1385 0.8202 3.1898
Coefficients:
Estimate Std. Error z value Pr(>|z|)
(Intercept) 2.226907 0.331142 6.725 1.76e-11 ***
GSEGRD -0.067798 0.014322 -4.734 2.20e-06 ***
LowerLimitAge -0.044612 0.002615 -17.062 < 2e-16 ***
BLS_FEDERAL_OtherSep_Rate 3.292260 0.292708 11.248 < 2e-16 ***
BLS_FEDERAL_Quits_Rate 0.457848 0.312029 1.467 0.142289
BLS_FEDERAL_JobOpenings_Level 0.017193 0.001846 9.313 < 2e-16 ***
LOSSqrt -0.777026 0.021815 -35.619 < 2e-16 ***
SEPCount_EFDATE_OCCLog -0.032260 0.015999 -2.016 0.043756 *
AGELVL_B1 -0.585577 0.219366 -2.669 0.007599 **
AGELVL_C1 -0.169029 0.085216 -1.984 0.047308 *
AGELVL_E1 0.276792 0.067193 4.119 3.80e-05 ***
AGELVL_F1 0.494216 0.069926 7.068 1.58e-12 ***
AGELVL_G1 0.714636 0.073806 9.683 < 2e-16 ***
AGELVL_H1 0.850740 0.076053 11.186 < 2e-16 ***
AGELVL_I1 0.718288 0.080973 8.871 < 2e-16 ***
LOC_011 -0.249365 0.164638 -1.515 0.129867
LOC_041 0.483733 0.133002 3.637 0.000276 ***
LOC_061 0.379766 0.082703 4.592 4.39e-06 ***
LOC_131 0.130493 0.111551 1.170 0.242078
LOC_171 -0.256802 0.141244 -1.818 0.069043 .
LOC_181 -0.269230 0.248412 -1.084 0.278452
LOC_201 0.571855 0.253077 2.260 0.023845 *
LOC_241 -0.144733 0.078588 -1.842 0.065524 .
LOC_271 0.248972 0.234803 1.060 0.288988
LOC_291 -0.390336 0.165618 -2.357 0.018431 *
LOC_301 0.637463 0.237019 2.690 0.007156 **
LOC_341 -0.213335 0.192601 -1.108 0.268013
LOC_351 0.428721 0.147175 2.913 0.003580 **
LOC_381 0.445638 0.364337 1.223 0.221273
LOC_391 -0.326822 0.139010 -2.351 0.018720 *
LOC_421 -0.334140 0.140201 -2.383 0.017158 *
LOC_461 0.770976 0.250781 3.074 0.002110 **
LOC_471 -0.221455 0.206720 -1.071 0.284043
LOC_481 0.213721 0.087681 2.437 0.014789 *
LOC_491 -0.224866 0.205604 -1.094 0.274093
LOC_511 -0.188720 0.073650 -2.562 0.010396 *
LOC_531 0.361285 0.124473 2.903 0.003702 **
LOC_551 -0.359539 0.247646 -1.452 0.146550
TOA_151 0.071768 0.068454 1.048 0.294451
TOA_201 1.131611 0.192559 5.877 4.19e-09 ***
TOA_301 0.342589 0.087457 3.917 8.96e-05 ***
TOA_351 -0.552669 0.285874 -1.933 0.053204 .
TOA_381 0.332616 0.066297 5.017 5.25e-07 ***
TOA_421 1.136968 0.441078 2.578 0.009946 **
TOA_441 2.139424 1.071581 1.997 0.045878 *
PPGROUP_111 -0.409204 0.116506 -3.512 0.000444 ***
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
(Dispersion parameter for binomial family taken to be 1)
Null deviance: 20683 on 14919 degrees of freedom
Residual deviance: 14552 on 14874 degrees of freedom
AIC: 14644
Number of Fisher Scoring iterations: 5
VIF
GSEGRD 1.471910
LowerLimitAge 2.246864
BLS_FEDERAL_OtherSep_Rate 1.339985
BLS_FEDERAL_Quits_Rate 1.210308
BLS_FEDERAL_JobOpenings_Level 1.088069
LOSSqrt 1.687659
SEPCount_EFDATE_OCCLog 1.094374
AGELVL_B 1.162369
AGELVL_C 1.657200
AGELVL_E 1.402616
AGELVL_F 1.278362
AGELVL_G 1.264575
AGELVL_H 1.356430
AGELVL_I 1.512358
LOC_01 1.028245
LOC_04 1.061789
LOC_06 1.110438
LOC_13 1.062187
LOC_17 1.035886
LOC_18 1.016929
LOC_20 1.014500
LOC_24 1.153692
LOC_27 1.016974
LOC_29 1.030603
LOC_30 1.021874
LOC_34 1.024240
LOC_35 1.049512
LOC_38 1.009706
LOC_39 1.040243
LOC_42 1.041116
LOC_46 1.021645
LOC_47 1.019330
LOC_48 1.111513
LOC_49 1.018985
LOC_51 1.150611
LOC_53 1.052689
LOC_55 1.018762
TOA_15 1.379999
TOA_20 1.025042
TOA_30 1.200666
TOA_35 1.071887
TOA_38 1.144507
TOA_42 1.009210
TOA_44 1.002803
PPGROUP_11 1.146013
Removed AFTER this step: TOA_15
Call:
glm(formula = fla, family = "binomial", data = OPMAnalysisDataNoFamBinary)
Deviance Residuals:
Min 1Q Median 3Q Max
-2.8323 -0.7406 -0.1371 0.8196 3.1978
Coefficients:
Estimate Std. Error z value Pr(>|z|)
(Intercept) 2.284875 0.326509 6.998 2.60e-12 ***
GSEGRD -0.069789 0.014189 -4.918 8.72e-07 ***
LowerLimitAge -0.044400 0.002606 -17.038 < 2e-16 ***
BLS_FEDERAL_OtherSep_Rate 3.293957 0.292777 11.251 < 2e-16 ***
BLS_FEDERAL_Quits_Rate 0.457266 0.311974 1.466 0.142725
BLS_FEDERAL_JobOpenings_Level 0.017202 0.001846 9.321 < 2e-16 ***
LOSSqrt -0.786305 0.019980 -39.355 < 2e-16 ***
SEPCount_EFDATE_OCCLog -0.031685 0.015983 -1.982 0.047433 *
AGELVL_B1 -0.565726 0.218381 -2.591 0.009582 **
AGELVL_C1 -0.168417 0.085158 -1.978 0.047963 *
AGELVL_E1 0.278146 0.067148 4.142 3.44e-05 ***
AGELVL_F1 0.496050 0.069879 7.099 1.26e-12 ***
AGELVL_G1 0.716975 0.073778 9.718 < 2e-16 ***
AGELVL_H1 0.852558 0.076067 11.208 < 2e-16 ***
AGELVL_I1 0.721587 0.080969 8.912 < 2e-16 ***
LOC_011 -0.250835 0.164729 -1.523 0.127830
LOC_041 0.484749 0.132912 3.647 0.000265 ***
LOC_061 0.378318 0.082724 4.573 4.80e-06 ***
LOC_131 0.129046 0.111516 1.157 0.247190
LOC_171 -0.259971 0.141227 -1.841 0.065651 .
LOC_181 -0.266334 0.248365 -1.072 0.283562
LOC_201 0.576614 0.252964 2.279 0.022642 *
LOC_241 -0.143268 0.078551 -1.824 0.068170 .
LOC_271 0.248636 0.234774 1.059 0.289580
LOC_291 -0.393320 0.165620 -2.375 0.017557 *
LOC_301 0.635658 0.237057 2.681 0.007330 **
LOC_341 -0.214443 0.192700 -1.113 0.265779
LOC_351 0.429215 0.147113 2.918 0.003528 **
LOC_381 0.444926 0.364179 1.222 0.221813
LOC_391 -0.326725 0.139036 -2.350 0.018777 *
LOC_421 -0.335981 0.140213 -2.396 0.016565 *
LOC_461 0.769164 0.250718 3.068 0.002156 **
LOC_471 -0.223466 0.206729 -1.081 0.279714
LOC_481 0.212147 0.087672 2.420 0.015530 *
LOC_491 -0.223720 0.205395 -1.089 0.276056
LOC_511 -0.185844 0.073526 -2.528 0.011485 *
LOC_531 0.360595 0.124424 2.898 0.003754 **
LOC_551 -0.362465 0.247744 -1.463 0.143450
TOA_201 1.111302 0.191863 5.792 6.95e-09 ***
TOA_301 0.333212 0.087144 3.824 0.000131 ***
TOA_351 -0.590290 0.283744 -2.080 0.037492 *
TOA_381 0.315580 0.064374 4.902 9.47e-07 ***
TOA_421 1.116051 0.440658 2.533 0.011319 *
TOA_441 2.133573 1.071743 1.991 0.046508 *
PPGROUP_111 -0.418532 0.116104 -3.605 0.000312 ***
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
(Dispersion parameter for binomial family taken to be 1)
Null deviance: 20683 on 14919 degrees of freedom
Residual deviance: 14553 on 14875 degrees of freedom
AIC: 14643
Number of Fisher Scoring iterations: 5
VIF
GSEGRD 1.446463
LowerLimitAge 2.231811
BLS_FEDERAL_OtherSep_Rate 1.339932
BLS_FEDERAL_Quits_Rate 1.210258
BLS_FEDERAL_JobOpenings_Level 1.088234
LOSSqrt 1.412967
SEPCount_EFDATE_OCCLog 1.093163
AGELVL_B 1.153319
AGELVL_C 1.657331
AGELVL_E 1.401872
AGELVL_F 1.277556
AGELVL_G 1.263549
AGELVL_H 1.355802
AGELVL_I 1.509972
LOC_01 1.028137
LOC_04 1.061851
LOC_06 1.110070
LOC_13 1.062088
LOC_17 1.035430
LOC_18 1.016804
LOC_20 1.014193
LOC_24 1.153416
LOC_27 1.016966
LOC_29 1.030312
LOC_30 1.021783
LOC_34 1.024156
LOC_35 1.049494
LOC_38 1.009721
LOC_39 1.040222
LOC_42 1.040943
LOC_46 1.021593
LOC_47 1.019240
LOC_48 1.111186
LOC_49 1.019008
LOC_51 1.149432
LOC_53 1.052728
LOC_55 1.018643
TOA_20 1.014651
TOA_30 1.187990
TOA_35 1.054678
TOA_38 1.075975
TOA_42 1.007166
TOA_44 1.002774
PPGROUP_11 1.139765
Removed AFTER this step: LOC_27
Call:
glm(formula = fla, family = "binomial", data = OPMAnalysisDataNoFamBinary)
Deviance Residuals:
Min 1Q Median 3Q Max
-2.8336 -0.7410 -0.1372 0.8201 3.1970
Coefficients:
Estimate Std. Error z value Pr(>|z|)
(Intercept) 2.300668 0.326157 7.054 1.74e-12 ***
GSEGRD -0.070581 0.014170 -4.981 6.32e-07 ***
LowerLimitAge -0.044447 0.002606 -17.059 < 2e-16 ***
BLS_FEDERAL_OtherSep_Rate 3.295665 0.292769 11.257 < 2e-16 ***
BLS_FEDERAL_Quits_Rate 0.456128 0.311950 1.462 0.143692
BLS_FEDERAL_JobOpenings_Level 0.017221 0.001845 9.333 < 2e-16 ***
LOSSqrt -0.785966 0.019975 -39.348 < 2e-16 ***
SEPCount_EFDATE_OCCLog -0.031742 0.015980 -1.986 0.046994 *
AGELVL_B1 -0.565670 0.218332 -2.591 0.009573 **
AGELVL_C1 -0.169989 0.085146 -1.996 0.045886 *
AGELVL_E1 0.277912 0.067145 4.139 3.49e-05 ***
AGELVL_F1 0.496279 0.069870 7.103 1.22e-12 ***
AGELVL_G1 0.717127 0.073770 9.721 < 2e-16 ***
AGELVL_H1 0.852763 0.076065 11.211 < 2e-16 ***
AGELVL_I1 0.721527 0.080966 8.911 < 2e-16 ***
LOC_011 -0.256069 0.164678 -1.555 0.119953
LOC_041 0.479225 0.132815 3.608 0.000308 ***
LOC_061 0.373008 0.082582 4.517 6.28e-06 ***
LOC_131 0.123943 0.111423 1.112 0.265980
LOC_171 -0.265129 0.141155 -1.878 0.060342 .
LOC_181 -0.272070 0.248331 -1.096 0.273256
LOC_201 0.571208 0.252935 2.258 0.023926 *
LOC_241 -0.147705 0.078447 -1.883 0.059720 .
LOC_291 -0.399017 0.165550 -2.410 0.015941 *
LOC_301 0.629863 0.237013 2.658 0.007872 **
LOC_341 -0.219640 0.192653 -1.140 0.254251
LOC_351 0.423717 0.147036 2.882 0.003955 **
LOC_381 0.439068 0.364170 1.206 0.227945
LOC_391 -0.332207 0.138955 -2.391 0.016814 *
LOC_421 -0.341250 0.140134 -2.435 0.014885 *
LOC_461 0.763145 0.250674 3.044 0.002332 **
LOC_471 -0.229106 0.206682 -1.108 0.267648
LOC_481 0.206884 0.087539 2.363 0.018111 *
LOC_491 -0.229129 0.205349 -1.116 0.264507
LOC_511 -0.190784 0.073391 -2.600 0.009334 **
LOC_531 0.355131 0.124325 2.856 0.004284 **
LOC_551 -0.368640 0.247701 -1.488 0.136686
TOA_201 1.111609 0.191918 5.792 6.95e-09 ***
TOA_301 0.332203 0.087146 3.812 0.000138 ***
TOA_351 -0.594681 0.283732 -2.096 0.036089 *
TOA_381 0.317449 0.064335 4.934 8.04e-07 ***
TOA_421 1.114271 0.440688 2.528 0.011456 *
TOA_441 2.130680 1.071735 1.988 0.046804 *
PPGROUP_111 -0.420026 0.116101 -3.618 0.000297 ***
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
(Dispersion parameter for binomial family taken to be 1)
Null deviance: 20683 on 14919 degrees of freedom
Residual deviance: 14554 on 14876 degrees of freedom
AIC: 14642
Number of Fisher Scoring iterations: 5
VIF
GSEGRD 1.442709
LowerLimitAge 2.231148
BLS_FEDERAL_OtherSep_Rate 1.339987
BLS_FEDERAL_Quits_Rate 1.210271
BLS_FEDERAL_JobOpenings_Level 1.088134
LOSSqrt 1.412203
SEPCount_EFDATE_OCCLog 1.093173
AGELVL_B 1.153381
AGELVL_C 1.656663
AGELVL_E 1.401770
AGELVL_F 1.277569
AGELVL_G 1.263577
AGELVL_H 1.355800
AGELVL_I 1.509930
LOC_01 1.027220
LOC_04 1.060244
LOC_06 1.106053
LOC_13 1.060145
LOC_17 1.034230
LOC_18 1.016329
LOC_20 1.013785
LOC_24 1.150238
LOC_29 1.029246
LOC_30 1.021240
LOC_34 1.023515
LOC_35 1.048198
LOC_38 1.009493
LOC_39 1.038804
LOC_42 1.039667
LOC_46 1.021069
LOC_47 1.018572
LOC_48 1.107684
LOC_49 1.018392
LOC_51 1.144940
LOC_53 1.050951
LOC_55 1.018087
TOA_20 1.014639
TOA_30 1.187852
TOA_35 1.054407
TOA_38 1.075292
TOA_42 1.007154
TOA_44 1.002767
PPGROUP_11 1.139572
Removed AFTER this step: LOC_18
Call:
glm(formula = fla, family = "binomial", data = OPMAnalysisDataNoFamBinary)
Deviance Residuals:
Min 1Q Median 3Q Max
-2.8336 -0.7405 -0.1369 0.8203 3.1984
Coefficients:
Estimate Std. Error z value Pr(>|z|)
(Intercept) 2.301357 0.326167 7.056 1.72e-12 ***
GSEGRD -0.070413 0.014169 -4.970 6.71e-07 ***
LowerLimitAge -0.044479 0.002605 -17.072 < 2e-16 ***
BLS_FEDERAL_OtherSep_Rate 3.296003 0.292769 11.258 < 2e-16 ***
BLS_FEDERAL_Quits_Rate 0.449135 0.311856 1.440 0.149811
BLS_FEDERAL_JobOpenings_Level 0.017208 0.001845 9.326 < 2e-16 ***
LOSSqrt -0.786147 0.019972 -39.362 < 2e-16 ***
SEPCount_EFDATE_OCCLog -0.031777 0.015980 -1.988 0.046757 *
AGELVL_B1 -0.564125 0.218307 -2.584 0.009764 **
AGELVL_C1 -0.171217 0.085135 -2.011 0.044313 *
AGELVL_E1 0.278207 0.067141 4.144 3.42e-05 ***
AGELVL_F1 0.496232 0.069867 7.102 1.23e-12 ***
AGELVL_G1 0.719013 0.073749 9.749 < 2e-16 ***
AGELVL_H1 0.854416 0.076043 11.236 < 2e-16 ***
AGELVL_I1 0.722358 0.080969 8.921 < 2e-16 ***
LOC_011 -0.251154 0.164606 -1.526 0.127061
LOC_041 0.483948 0.132751 3.646 0.000267 ***
LOC_061 0.377941 0.082455 4.584 4.57e-06 ***
LOC_131 0.128419 0.111346 1.153 0.248773
LOC_171 -0.260465 0.141080 -1.846 0.064859 .
LOC_201 0.575914 0.252898 2.277 0.022771 *
LOC_241 -0.143501 0.078351 -1.832 0.067022 .
LOC_291 -0.393978 0.165474 -2.381 0.017271 *
LOC_301 0.634601 0.236984 2.678 0.007410 **
LOC_341 -0.214724 0.192593 -1.115 0.264888
LOC_351 0.428411 0.146977 2.915 0.003559 **
LOC_381 0.443876 0.364125 1.219 0.222837
LOC_391 -0.327121 0.138869 -2.356 0.018493 *
LOC_421 -0.336346 0.140058 -2.401 0.016329 *
LOC_461 0.768163 0.250645 3.065 0.002179 **
LOC_471 -0.223872 0.206609 -1.084 0.278562
LOC_481 0.211590 0.087432 2.420 0.015519 *
LOC_491 -0.224250 0.205287 -1.092 0.274669
LOC_511 -0.186489 0.073281 -2.545 0.010933 *
LOC_531 0.359848 0.124253 2.896 0.003778 **
LOC_551 -0.362699 0.247606 -1.465 0.142970
TOA_201 1.111397 0.191913 5.791 6.99e-09 ***
TOA_301 0.333743 0.087136 3.830 0.000128 ***
TOA_351 -0.592970 0.283704 -2.090 0.036609 *
TOA_381 0.313658 0.064233 4.883 1.04e-06 ***
TOA_421 1.115476 0.440672 2.531 0.011364 *
TOA_441 2.134395 1.071723 1.992 0.046420 *
PPGROUP_111 -0.421494 0.116111 -3.630 0.000283 ***
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
(Dispersion parameter for binomial family taken to be 1)
Null deviance: 20683 on 14919 degrees of freedom
Residual deviance: 14555 on 14877 degrees of freedom
AIC: 14641
Number of Fisher Scoring iterations: 5
VIF
GSEGRD 1.442606
LowerLimitAge 2.231047
BLS_FEDERAL_OtherSep_Rate 1.339971
BLS_FEDERAL_Quits_Rate 1.209863
BLS_FEDERAL_JobOpenings_Level 1.088031
LOSSqrt 1.412203
SEPCount_EFDATE_OCCLog 1.093142
AGELVL_B 1.153281
AGELVL_C 1.656357
AGELVL_E 1.401727
AGELVL_F 1.277552
AGELVL_G 1.262980
AGELVL_H 1.355447
AGELVL_I 1.509768
LOC_01 1.026454
LOC_04 1.059092
LOC_06 1.102722
LOC_13 1.058690
LOC_17 1.033273
LOC_20 1.013485
LOC_24 1.147435
LOC_29 1.028436
LOC_30 1.020893
LOC_34 1.022944
LOC_35 1.047286
LOC_38 1.009342
LOC_39 1.037628
LOC_42 1.038581
LOC_46 1.020717
LOC_47 1.018020
LOC_48 1.104957
LOC_49 1.017906
LOC_51 1.141582
LOC_53 1.049660
LOC_55 1.017595
TOA_20 1.014600
TOA_30 1.187619
TOA_35 1.054347
TOA_38 1.072010
TOA_42 1.007143
TOA_44 1.002758
PPGROUP_11 1.139384
Removed AFTER this step: LOC_47
Call:
glm(formula = fla, family = "binomial", data = OPMAnalysisDataNoFamBinary)
Deviance Residuals:
Min 1Q Median 3Q Max
-2.8334 -0.7413 -0.1372 0.8200 3.2002
Coefficients:
Estimate Std. Error z value Pr(>|z|)
(Intercept) 2.288947 0.325910 7.023 2.17e-12 ***
GSEGRD -0.069972 0.014162 -4.941 7.78e-07 ***
LowerLimitAge -0.044529 0.002605 -17.092 < 2e-16 ***
BLS_FEDERAL_OtherSep_Rate 3.299628 0.292740 11.272 < 2e-16 ***
BLS_FEDERAL_Quits_Rate 0.446453 0.311816 1.432 0.152205
BLS_FEDERAL_JobOpenings_Level 0.017222 0.001845 9.335 < 2e-16 ***
LOSSqrt -0.786202 0.019970 -39.369 < 2e-16 ***
SEPCount_EFDATE_OCCLog -0.031781 0.015979 -1.989 0.046714 *
AGELVL_B1 -0.564885 0.218235 -2.588 0.009642 **
AGELVL_C1 -0.170353 0.085129 -2.001 0.045379 *
AGELVL_E1 0.278912 0.067136 4.154 3.26e-05 ***
AGELVL_F1 0.496473 0.069861 7.107 1.19e-12 ***
AGELVL_G1 0.720110 0.073744 9.765 < 2e-16 ***
AGELVL_H1 0.856021 0.076022 11.260 < 2e-16 ***
AGELVL_I1 0.723021 0.080974 8.929 < 2e-16 ***
LOC_011 -0.245622 0.164524 -1.493 0.135455
LOC_041 0.490164 0.132634 3.696 0.000219 ***
LOC_061 0.383615 0.082290 4.662 3.14e-06 ***
LOC_131 0.134002 0.111229 1.205 0.228304
LOC_171 -0.255039 0.140990 -1.809 0.070465 .
LOC_201 0.581832 0.252840 2.301 0.021381 *
LOC_241 -0.138352 0.078206 -1.769 0.076882 .
LOC_291 -0.388107 0.165381 -2.347 0.018939 *
LOC_301 0.640959 0.236913 2.705 0.006821 **
LOC_341 -0.209339 0.192531 -1.087 0.276905
LOC_351 0.434482 0.146874 2.958 0.003094 **
LOC_381 0.450007 0.364087 1.236 0.216463
LOC_391 -0.321330 0.138766 -2.316 0.020579 *
LOC_421 -0.330852 0.139968 -2.364 0.018090 *
LOC_461 0.774567 0.250587 3.091 0.001995 **
LOC_481 0.217435 0.087267 2.492 0.012717 *
LOC_491 -0.218615 0.205227 -1.065 0.286769
LOC_511 -0.181206 0.073113 -2.478 0.013196 *
LOC_531 0.365528 0.124149 2.944 0.003237 **
LOC_551 -0.356485 0.247535 -1.440 0.149828
TOA_201 1.111930 0.191968 5.792 6.94e-09 ***
TOA_301 0.334832 0.087134 3.843 0.000122 ***
TOA_351 -0.589765 0.283667 -2.079 0.037611 *
TOA_381 0.312202 0.064217 4.862 1.16e-06 ***
TOA_421 1.117389 0.440662 2.536 0.011222 *
TOA_441 2.138344 1.071717 1.995 0.046016 *
PPGROUP_111 -0.419447 0.116082 -3.613 0.000302 ***
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
(Dispersion parameter for binomial family taken to be 1)
Null deviance: 20683 on 14919 degrees of freedom
Residual deviance: 14556 on 14878 degrees of freedom
AIC: 14640
Number of Fisher Scoring iterations: 5
VIF
GSEGRD 1.441478
LowerLimitAge 2.230723
BLS_FEDERAL_OtherSep_Rate 1.339728
BLS_FEDERAL_Quits_Rate 1.209663
BLS_FEDERAL_JobOpenings_Level 1.088000
LOSSqrt 1.412143
SEPCount_EFDATE_OCCLog 1.093141
AGELVL_B 1.153341
AGELVL_C 1.656215
AGELVL_E 1.401687
AGELVL_F 1.277689
AGELVL_G 1.262870
AGELVL_H 1.355321
AGELVL_I 1.510002
LOC_01 1.025462
LOC_04 1.057098
LOC_06 1.098240
LOC_13 1.056395
LOC_17 1.031951
LOC_20 1.013009
LOC_24 1.143145
LOC_29 1.027318
LOC_30 1.020271
LOC_34 1.022244
LOC_35 1.045751
LOC_38 1.009095
LOC_39 1.036075
LOC_42 1.037193
LOC_46 1.020145
LOC_48 1.100711
LOC_49 1.017242
LOC_51 1.136432
LOC_53 1.047766
LOC_55 1.017044
TOA_20 1.014611
TOA_30 1.187469
TOA_35 1.054241
TOA_38 1.071490
TOA_42 1.007120
TOA_44 1.002746
PPGROUP_11 1.139105
Removed AFTER this step: LOC_49
Call:
glm(formula = fla, family = "binomial", data = OPMAnalysisDataNoFamBinary)
Deviance Residuals:
Min 1Q Median 3Q Max
-2.8326 -0.7416 -0.1372 0.8194 3.2019
Coefficients:
Estimate Std. Error z value Pr(>|z|)
(Intercept) 2.279301 0.325773 6.997 2.62e-12 ***
GSEGRD -0.069673 0.014159 -4.921 8.62e-07 ***
LowerLimitAge -0.044545 0.002605 -17.101 < 2e-16 ***
BLS_FEDERAL_OtherSep_Rate 3.299455 0.292722 11.272 < 2e-16 ***
BLS_FEDERAL_Quits_Rate 0.451147 0.311784 1.447 0.147900
BLS_FEDERAL_JobOpenings_Level 0.017219 0.001845 9.333 < 2e-16 ***
LOSSqrt -0.786288 0.019970 -39.374 < 2e-16 ***
SEPCount_EFDATE_OCCLog -0.031662 0.015978 -1.982 0.047529 *
AGELVL_B1 -0.563039 0.218217 -2.580 0.009875 **
AGELVL_C1 -0.171589 0.085118 -2.016 0.043810 *
AGELVL_E1 0.278193 0.067126 4.144 3.41e-05 ***
AGELVL_F1 0.496255 0.069865 7.103 1.22e-12 ***
AGELVL_G1 0.720114 0.073742 9.765 < 2e-16 ***
AGELVL_H1 0.855562 0.076011 11.256 < 2e-16 ***
AGELVL_I1 0.721648 0.080954 8.914 < 2e-16 ***
LOC_011 -0.240162 0.164448 -1.460 0.144177
LOC_041 0.495796 0.132532 3.741 0.000183 ***
LOC_061 0.389091 0.082130 4.737 2.16e-06 ***
LOC_131 0.139458 0.111112 1.255 0.209439
LOC_171 -0.249710 0.140907 -1.772 0.076368 .
LOC_201 0.587493 0.252799 2.324 0.020128 *
LOC_241 -0.133149 0.078053 -1.706 0.088030 .
LOC_291 -0.382416 0.165295 -2.314 0.020693 *
LOC_301 0.646869 0.236860 2.731 0.006314 **
LOC_341 -0.203926 0.192461 -1.060 0.289340
LOC_351 0.440065 0.146783 2.998 0.002717 **
LOC_381 0.455832 0.364070 1.252 0.210554
LOC_391 -0.315969 0.138678 -2.278 0.022700 *
LOC_421 -0.325593 0.139882 -2.328 0.019932 *
LOC_461 0.780279 0.250534 3.114 0.001843 **
LOC_481 0.222916 0.087116 2.559 0.010502 *
LOC_511 -0.175804 0.072933 -2.410 0.015931 *
LOC_531 0.371172 0.124039 2.992 0.002768 **
LOC_551 -0.350992 0.247486 -1.418 0.156124
TOA_201 1.110839 0.192020 5.785 7.25e-09 ***
TOA_301 0.337484 0.087101 3.875 0.000107 ***
TOA_351 -0.587042 0.283639 -2.070 0.038482 *
TOA_381 0.312530 0.064216 4.867 1.13e-06 ***
TOA_421 1.119737 0.440646 2.541 0.011050 *
TOA_441 2.143004 1.071728 2.000 0.045546 *
PPGROUP_111 -0.419963 0.116068 -3.618 0.000297 ***
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
(Dispersion parameter for binomial family taken to be 1)
Null deviance: 20683 on 14919 degrees of freedom
Residual deviance: 14557 on 14879 degrees of freedom
AIC: 14639
Number of Fisher Scoring iterations: 5
VIF
GSEGRD 1.440964
LowerLimitAge 2.230539
BLS_FEDERAL_OtherSep_Rate 1.339622
BLS_FEDERAL_Quits_Rate 1.209313
BLS_FEDERAL_JobOpenings_Level 1.088000
LOSSqrt 1.412197
SEPCount_EFDATE_OCCLog 1.093097
AGELVL_B 1.153190
AGELVL_C 1.655688
AGELVL_E 1.401531
AGELVL_F 1.277535
AGELVL_G 1.262762
AGELVL_H 1.355273
AGELVL_I 1.509508
LOC_01 1.024460
LOC_04 1.055396
LOC_06 1.093927
LOC_13 1.054135
LOC_17 1.030631
LOC_20 1.012557
LOC_24 1.138629
LOC_29 1.026235
LOC_30 1.019709
LOC_34 1.021518
LOC_35 1.044403
LOC_38 1.008862
LOC_39 1.034693
LOC_42 1.035878
LOC_46 1.019673
LOC_48 1.096847
LOC_51 1.130880
LOC_53 1.045840
LOC_55 1.016591
TOA_20 1.014529
TOA_30 1.186588
TOA_35 1.054118
TOA_38 1.071478
TOA_42 1.007089
TOA_44 1.002729
PPGROUP_11 1.139079
Removed AFTER this step: LOC_34
Call:
glm(formula = fla, family = "binomial", data = OPMAnalysisDataNoFamBinary)
Deviance Residuals:
Min 1Q Median 3Q Max
-2.8326 -0.7421 -0.1372 0.8190 3.2036
Coefficients:
Estimate Std. Error z value Pr(>|z|)
(Intercept) 2.273147 0.325680 6.980 2.96e-12 ***
GSEGRD -0.069754 0.014159 -4.927 8.37e-07 ***
LowerLimitAge -0.044516 0.002605 -17.091 < 2e-16 ***
BLS_FEDERAL_OtherSep_Rate 3.297426 0.292713 11.265 < 2e-16 ***
BLS_FEDERAL_Quits_Rate 0.451452 0.311778 1.448 0.147618
BLS_FEDERAL_JobOpenings_Level 0.017222 0.001845 9.336 < 2e-16 ***
LOSSqrt -0.786517 0.019967 -39.390 < 2e-16 ***
SEPCount_EFDATE_OCCLog -0.031516 0.015978 -1.973 0.048551 *
AGELVL_B1 -0.569066 0.218030 -2.610 0.009053 **
AGELVL_C1 -0.174880 0.085038 -2.056 0.039736 *
AGELVL_E1 0.278584 0.067129 4.150 3.33e-05 ***
AGELVL_F1 0.495746 0.069856 7.097 1.28e-12 ***
AGELVL_G1 0.720380 0.073743 9.769 < 2e-16 ***
AGELVL_H1 0.855502 0.076011 11.255 < 2e-16 ***
AGELVL_I1 0.722020 0.080959 8.918 < 2e-16 ***
LOC_011 -0.234490 0.164350 -1.427 0.153645
LOC_041 0.500919 0.132441 3.782 0.000155 ***
LOC_061 0.394540 0.081963 4.814 1.48e-06 ***
LOC_131 0.144818 0.110989 1.305 0.191963
LOC_171 -0.244189 0.140798 -1.734 0.082862 .
LOC_201 0.592712 0.252739 2.345 0.019019 *
LOC_241 -0.127418 0.077857 -1.637 0.101719
LOC_291 -0.377062 0.165209 -2.282 0.022469 *
LOC_301 0.651917 0.236812 2.753 0.005907 **
LOC_351 0.445382 0.146684 3.036 0.002395 **
LOC_381 0.461173 0.364045 1.267 0.205226
LOC_391 -0.310309 0.138559 -2.240 0.025120 *
LOC_421 -0.319727 0.139758 -2.288 0.022154 *
LOC_461 0.785319 0.250490 3.135 0.001718 **
LOC_481 0.228151 0.086971 2.623 0.008708 **
LOC_511 -0.170127 0.072727 -2.339 0.019321 *
LOC_531 0.376910 0.123906 3.042 0.002351 **
LOC_551 -0.345354 0.247416 -1.396 0.162761
TOA_201 1.110535 0.191952 5.785 7.23e-09 ***
TOA_301 0.339884 0.087059 3.904 9.46e-05 ***
TOA_351 -0.593078 0.283491 -2.092 0.036434 *
TOA_381 0.312282 0.064213 4.863 1.15e-06 ***
TOA_421 1.121734 0.440621 2.546 0.010903 *
TOA_441 2.148620 1.071715 2.005 0.044980 *
PPGROUP_111 -0.418722 0.116028 -3.609 0.000308 ***
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
(Dispersion parameter for binomial family taken to be 1)
Null deviance: 20683 on 14919 degrees of freedom
Residual deviance: 14559 on 14880 degrees of freedom
AIC: 14639
Number of Fisher Scoring iterations: 5
VIF
GSEGRD 1.441092
LowerLimitAge 2.230622
BLS_FEDERAL_OtherSep_Rate 1.339593
BLS_FEDERAL_Quits_Rate 1.209410
BLS_FEDERAL_JobOpenings_Level 1.087977
LOSSqrt 1.412039
SEPCount_EFDATE_OCCLog 1.093007
AGELVL_B 1.152407
AGELVL_C 1.653729
AGELVL_E 1.401295
AGELVL_F 1.277467
AGELVL_G 1.262676
AGELVL_H 1.355241
AGELVL_I 1.509331
LOC_01 1.023371
LOC_04 1.053965
LOC_06 1.089602
LOC_13 1.051921
LOC_17 1.029202
LOC_20 1.012167
LOC_24 1.133096
LOC_29 1.025262
LOC_30 1.019297
LOC_35 1.043170
LOC_38 1.008663
LOC_39 1.033141
LOC_42 1.034230
LOC_46 1.019298
LOC_48 1.093275
LOC_51 1.124673
LOC_53 1.043836
LOC_55 1.016109
TOA_20 1.014544
TOA_30 1.185853
TOA_35 1.053524
TOA_38 1.071444
TOA_42 1.007066
TOA_44 1.002704
PPGROUP_11 1.138933
Removed AFTER this step: LOC_38
Call:
glm(formula = fla, family = "binomial", data = OPMAnalysisDataNoFamBinary)
Deviance Residuals:
Min 1Q Median 3Q Max
-2.8331 -0.7421 -0.1371 0.8188 3.2035
Coefficients:
Estimate Std. Error z value Pr(>|z|)
(Intercept) 2.288737 0.325439 7.033 2.02e-12 ***
GSEGRD -0.070649 0.014141 -4.996 5.85e-07 ***
LowerLimitAge -0.044472 0.002604 -17.078 < 2e-16 ***
BLS_FEDERAL_OtherSep_Rate 3.297244 0.292701 11.265 < 2e-16 ***
BLS_FEDERAL_Quits_Rate 0.457003 0.311725 1.466 0.142635
BLS_FEDERAL_JobOpenings_Level 0.017221 0.001845 9.335 < 2e-16 ***
LOSSqrt -0.786469 0.019962 -39.397 < 2e-16 ***
SEPCount_EFDATE_OCCLog -0.031999 0.015973 -2.003 0.045144 *
AGELVL_B1 -0.571840 0.218034 -2.623 0.008723 **
AGELVL_C1 -0.174323 0.085040 -2.050 0.040375 *
AGELVL_E1 0.279111 0.067125 4.158 3.21e-05 ***
AGELVL_F1 0.495977 0.069848 7.101 1.24e-12 ***
AGELVL_G1 0.721810 0.073732 9.790 < 2e-16 ***
AGELVL_H1 0.855262 0.076008 11.252 < 2e-16 ***
AGELVL_I1 0.722810 0.080945 8.930 < 2e-16 ***
LOC_011 -0.238067 0.164338 -1.449 0.147438
LOC_041 0.496477 0.132394 3.750 0.000177 ***
LOC_061 0.390781 0.081910 4.771 1.83e-06 ***
LOC_131 0.141148 0.110952 1.272 0.203317
LOC_171 -0.247711 0.140773 -1.760 0.078468 .
LOC_201 0.588347 0.252720 2.328 0.019909 *
LOC_241 -0.130644 0.077820 -1.679 0.093194 .
LOC_291 -0.380884 0.165185 -2.306 0.021122 *
LOC_301 0.646994 0.236773 2.733 0.006285 **
LOC_351 0.441029 0.146651 3.007 0.002636 **
LOC_391 -0.314029 0.138532 -2.267 0.023401 *
LOC_421 -0.323189 0.139737 -2.313 0.020732 *
LOC_461 0.780437 0.250460 3.116 0.001833 **
LOC_481 0.224234 0.086918 2.580 0.009885 **
LOC_511 -0.173680 0.072684 -2.390 0.016870 *
LOC_531 0.372853 0.123865 3.010 0.002611 **
LOC_551 -0.349191 0.247385 -1.412 0.158089
TOA_201 1.108204 0.191942 5.774 7.76e-09 ***
TOA_301 0.340300 0.087034 3.910 9.23e-05 ***
TOA_351 -0.596965 0.283500 -2.106 0.035230 *
TOA_381 0.311270 0.064192 4.849 1.24e-06 ***
TOA_421 1.137031 0.440440 2.582 0.009835 **
TOA_441 2.148009 1.071708 2.004 0.045039 *
PPGROUP_111 -0.422552 0.115970 -3.644 0.000269 ***
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
(Dispersion parameter for binomial family taken to be 1)
Null deviance: 20683 on 14919 degrees of freedom
Residual deviance: 14560 on 14881 degrees of freedom
AIC: 14638
Number of Fisher Scoring iterations: 5
VIF
GSEGRD 1.437401
LowerLimitAge 2.229757
BLS_FEDERAL_OtherSep_Rate 1.339492
BLS_FEDERAL_Quits_Rate 1.209139
BLS_FEDERAL_JobOpenings_Level 1.087965
LOSSqrt 1.411618
SEPCount_EFDATE_OCCLog 1.092337
AGELVL_B 1.152332
AGELVL_C 1.653555
AGELVL_E 1.401297
AGELVL_F 1.277537
AGELVL_G 1.262409
AGELVL_H 1.355159
AGELVL_I 1.509413
LOC_01 1.023075
LOC_04 1.053224
LOC_06 1.088228
LOC_13 1.051226
LOC_17 1.028819
LOC_20 1.011976
LOC_24 1.131941
LOC_29 1.024934
LOC_30 1.019018
LOC_35 1.042590
LOC_39 1.032692
LOC_42 1.033845
LOC_46 1.019052
LOC_48 1.091917
LOC_51 1.123080
LOC_53 1.043162
LOC_55 1.015969
TOA_20 1.014444
TOA_30 1.185656
TOA_35 1.053449
TOA_38 1.071210
TOA_42 1.006519
TOA_44 1.002704
PPGROUP_11 1.138163
Removed AFTER this step: LOC_13
Call:
glm(formula = fla, family = "binomial", data = OPMAnalysisDataNoFamBinary)
Deviance Residuals:
Min 1Q Median 3Q Max
-2.8328 -0.7414 -0.1372 0.8197 3.1996
Coefficients:
Estimate Std. Error z value Pr(>|z|)
(Intercept) 2.305021 0.325167 7.089 1.35e-12 ***
GSEGRD -0.070803 0.014141 -5.007 5.53e-07 ***
LowerLimitAge -0.044453 0.002604 -17.071 < 2e-16 ***
BLS_FEDERAL_OtherSep_Rate 3.295540 0.292640 11.261 < 2e-16 ***
BLS_FEDERAL_Quits_Rate 0.454130 0.311663 1.457 0.145083
BLS_FEDERAL_JobOpenings_Level 0.017240 0.001845 9.346 < 2e-16 ***
LOSSqrt -0.786405 0.019959 -39.401 < 2e-16 ***
SEPCount_EFDATE_OCCLog -0.031412 0.015965 -1.968 0.049118 *
AGELVL_B1 -0.571853 0.217993 -2.623 0.008709 **
AGELVL_C1 -0.175126 0.085037 -2.059 0.039455 *
AGELVL_E1 0.279596 0.067122 4.165 3.11e-05 ***
AGELVL_F1 0.496321 0.069849 7.106 1.20e-12 ***
AGELVL_G1 0.722116 0.073718 9.796 < 2e-16 ***
AGELVL_H1 0.855454 0.075991 11.257 < 2e-16 ***
AGELVL_I1 0.723392 0.080948 8.937 < 2e-16 ***
LOC_011 -0.249110 0.164089 -1.518 0.128980
LOC_041 0.484148 0.132031 3.667 0.000245 ***
LOC_061 0.379716 0.081443 4.662 3.13e-06 ***
LOC_171 -0.258659 0.140490 -1.841 0.065604 .
LOC_201 0.576610 0.252545 2.283 0.022419 *
LOC_241 -0.142290 0.077279 -1.841 0.065586 .
LOC_291 -0.392200 0.164931 -2.378 0.017408 *
LOC_301 0.634859 0.236584 2.683 0.007287 **
LOC_351 0.428815 0.146332 2.930 0.003385 **
LOC_391 -0.325077 0.138246 -2.351 0.018701 *
LOC_421 -0.334075 0.139463 -2.395 0.016601 *
LOC_461 0.768194 0.250260 3.070 0.002144 **
LOC_481 0.212320 0.086411 2.457 0.014006 *
LOC_511 -0.185081 0.072134 -2.566 0.010294 *
LOC_531 0.361283 0.123526 2.925 0.003447 **
LOC_551 -0.359414 0.247226 -1.454 0.146006
TOA_201 1.108519 0.191835 5.779 7.54e-09 ***
TOA_301 0.334690 0.086931 3.850 0.000118 ***
TOA_351 -0.600851 0.283472 -2.120 0.034039 *
TOA_381 0.308129 0.064127 4.805 1.55e-06 ***
TOA_421 1.133616 0.440402 2.574 0.010052 *
TOA_441 2.136459 1.071660 1.994 0.046196 *
PPGROUP_111 -0.428159 0.115916 -3.694 0.000221 ***
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
(Dispersion parameter for binomial family taken to be 1)
Null deviance: 20683 on 14919 degrees of freedom
Residual deviance: 14562 on 14882 degrees of freedom
AIC: 14638
Number of Fisher Scoring iterations: 5
VIF
GSEGRD 1.437018
LowerLimitAge 2.229701
BLS_FEDERAL_OtherSep_Rate 1.339322
BLS_FEDERAL_Quits_Rate 1.209083
BLS_FEDERAL_JobOpenings_Level 1.087880
LOSSqrt 1.411623
SEPCount_EFDATE_OCCLog 1.091343
AGELVL_B 1.152194
AGELVL_C 1.653433
AGELVL_E 1.401295
AGELVL_F 1.277524
AGELVL_G 1.262512
AGELVL_H 1.355362
AGELVL_I 1.509405
LOC_01 1.020234
LOC_04 1.047587
LOC_06 1.076039
LOC_17 1.025023
LOC_20 1.010633
LOC_24 1.116448
LOC_29 1.021994
LOC_30 1.017351
LOC_35 1.038133
LOC_39 1.028675
LOC_42 1.030027
LOC_46 1.017548
LOC_48 1.079290
LOC_51 1.106252
LOC_53 1.037582
LOC_55 1.014920
TOA_20 1.014397
TOA_30 1.182600
TOA_35 1.053292
TOA_38 1.069624
TOA_42 1.006494
TOA_44 1.002632
PPGROUP_11 1.136280
Removed AFTER this step: LOC_55
Call:
glm(formula = fla, family = "binomial", data = OPMAnalysisDataNoFamBinary)
Deviance Residuals:
Min 1Q Median 3Q Max
-2.8304 -0.7424 -0.1372 0.8191 3.2006
Coefficients:
Estimate Std. Error z value Pr(>|z|)
(Intercept) 2.290859 0.324962 7.050 1.79e-12 ***
GSEGRD -0.070260 0.014133 -4.972 6.64e-07 ***
LowerLimitAge -0.044406 0.002604 -17.056 < 2e-16 ***
BLS_FEDERAL_OtherSep_Rate 3.304905 0.292561 11.296 < 2e-16 ***
BLS_FEDERAL_Quits_Rate 0.447237 0.311599 1.435 0.151202
BLS_FEDERAL_JobOpenings_Level 0.017242 0.001845 9.348 < 2e-16 ***
LOSSqrt -0.786744 0.019961 -39.414 < 2e-16 ***
SEPCount_EFDATE_OCCLog -0.031099 0.015960 -1.949 0.051348 .
AGELVL_B1 -0.569395 0.217909 -2.613 0.008975 **
AGELVL_C1 -0.173741 0.085016 -2.044 0.040989 *
AGELVL_E1 0.281498 0.067106 4.195 2.73e-05 ***
AGELVL_F1 0.498374 0.069823 7.138 9.49e-13 ***
AGELVL_G1 0.723541 0.073704 9.817 < 2e-16 ***
AGELVL_H1 0.856734 0.075979 11.276 < 2e-16 ***
AGELVL_I1 0.724646 0.080951 8.952 < 2e-16 ***
LOC_011 -0.243686 0.164018 -1.486 0.137351
LOC_041 0.489189 0.131983 3.706 0.000210 ***
LOC_061 0.385230 0.081346 4.736 2.18e-06 ***
LOC_171 -0.253347 0.140420 -1.804 0.071199 .
LOC_201 0.581768 0.252498 2.304 0.021220 *
LOC_241 -0.138029 0.077217 -1.788 0.073849 .
LOC_291 -0.386277 0.164852 -2.343 0.019120 *
LOC_301 0.640153 0.236546 2.706 0.006805 **
LOC_351 0.433813 0.146281 2.966 0.003021 **
LOC_391 -0.319236 0.138168 -2.310 0.020861 *
LOC_421 -0.328519 0.139396 -2.357 0.018436 *
LOC_461 0.773537 0.250218 3.091 0.001992 **
LOC_481 0.217318 0.086337 2.517 0.011833 *
LOC_511 -0.180347 0.072050 -2.503 0.012311 *
LOC_531 0.366559 0.123469 2.969 0.002989 **
TOA_201 1.106144 0.191853 5.766 8.14e-09 ***
TOA_301 0.335294 0.086920 3.857 0.000115 ***
TOA_351 -0.597167 0.283439 -2.107 0.035130 *
TOA_381 0.302901 0.064015 4.732 2.23e-06 ***
TOA_421 1.133028 0.440466 2.572 0.010101 *
TOA_441 2.139104 1.071653 1.996 0.045925 *
PPGROUP_111 -0.429486 0.115917 -3.705 0.000211 ***
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
(Dispersion parameter for binomial family taken to be 1)
Null deviance: 20683 on 14919 degrees of freedom
Residual deviance: 14564 on 14883 degrees of freedom
AIC: 14638
Number of Fisher Scoring iterations: 5
VIF
GSEGRD 1.435977
LowerLimitAge 2.229347
BLS_FEDERAL_OtherSep_Rate 1.338907
BLS_FEDERAL_Quits_Rate 1.208788
BLS_FEDERAL_JobOpenings_Level 1.087945
LOSSqrt 1.411848
SEPCount_EFDATE_OCCLog 1.091253
AGELVL_B 1.152168
AGELVL_C 1.653359
AGELVL_E 1.400710
AGELVL_F 1.277035
AGELVL_G 1.262333
AGELVL_H 1.355247
AGELVL_I 1.509236
LOC_01 1.019713
LOC_04 1.046827
LOC_06 1.073693
LOC_17 1.024316
LOC_20 1.010429
LOC_24 1.114786
LOC_29 1.021359
LOC_30 1.017101
LOC_35 1.037548
LOC_39 1.027798
LOC_42 1.029238
LOC_46 1.017318
LOC_48 1.077540
LOC_51 1.103901
LOC_53 1.036661
TOA_20 1.014267
TOA_30 1.182683
TOA_35 1.053172
TOA_38 1.065754
TOA_42 1.006473
TOA_44 1.002628
PPGROUP_11 1.136089
Removed AFTER this step: BLS_FEDERAL_Quits_Rate
Call:
glm(formula = fla, family = "binomial", data = OPMAnalysisDataNoFamBinary)
Deviance Residuals:
Min 1Q Median 3Q Max
-2.8339 -0.7430 -0.1386 0.8205 3.1825
Coefficients:
Estimate Std. Error z value Pr(>|z|)
(Intercept) 2.410808 0.314127 7.675 1.66e-14 ***
GSEGRD -0.070410 0.014130 -4.983 6.26e-07 ***
LowerLimitAge -0.044487 0.002603 -17.092 < 2e-16 ***
BLS_FEDERAL_OtherSep_Rate 3.476209 0.267379 13.001 < 2e-16 ***
BLS_FEDERAL_JobOpenings_Level 0.017455 0.001840 9.487 < 2e-16 ***
LOSSqrt -0.787113 0.019958 -39.439 < 2e-16 ***
SEPCount_EFDATE_OCCLog -0.030764 0.015957 -1.928 0.053873 .
AGELVL_B1 -0.564717 0.217824 -2.593 0.009527 **
AGELVL_C1 -0.174318 0.085000 -2.051 0.040287 *
AGELVL_E1 0.281212 0.067103 4.191 2.78e-05 ***
AGELVL_F1 0.497914 0.069818 7.132 9.92e-13 ***
AGELVL_G1 0.724310 0.073687 9.829 < 2e-16 ***
AGELVL_H1 0.856605 0.075975 11.275 < 2e-16 ***
AGELVL_I1 0.724080 0.080943 8.946 < 2e-16 ***
LOC_011 -0.244474 0.164008 -1.491 0.136061
LOC_041 0.492762 0.131928 3.735 0.000188 ***
LOC_061 0.385996 0.081350 4.745 2.09e-06 ***
LOC_171 -0.252538 0.140354 -1.799 0.071972 .
LOC_201 0.586532 0.252542 2.323 0.020205 *
LOC_241 -0.137812 0.077207 -1.785 0.074267 .
LOC_291 -0.387618 0.164849 -2.351 0.018705 *
LOC_301 0.639158 0.236775 2.699 0.006946 **
LOC_351 0.435321 0.146296 2.976 0.002924 **
LOC_391 -0.317610 0.138174 -2.299 0.021526 *
LOC_421 -0.328389 0.139440 -2.355 0.018520 *
LOC_461 0.777644 0.250480 3.105 0.001905 **
LOC_481 0.218995 0.086324 2.537 0.011184 *
LOC_511 -0.181523 0.072039 -2.520 0.011742 *
LOC_531 0.368145 0.123466 2.982 0.002866 **
TOA_201 1.104861 0.191762 5.762 8.33e-09 ***
TOA_301 0.336799 0.086915 3.875 0.000107 ***
TOA_351 -0.595588 0.283317 -2.102 0.035536 *
TOA_381 0.305170 0.064005 4.768 1.86e-06 ***
TOA_421 1.130290 0.440326 2.567 0.010260 *
TOA_441 2.136749 1.071254 1.995 0.046084 *
PPGROUP_111 -0.429273 0.115908 -3.704 0.000213 ***
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
(Dispersion parameter for binomial family taken to be 1)
Null deviance: 20683 on 14919 degrees of freedom
Residual deviance: 14566 on 14884 degrees of freedom
AIC: 14638
Number of Fisher Scoring iterations: 5
VIF
GSEGRD 1.435633
LowerLimitAge 2.228674
BLS_FEDERAL_OtherSep_Rate 1.117995
BLS_FEDERAL_JobOpenings_Level 1.081106
LOSSqrt 1.411994
SEPCount_EFDATE_OCCLog 1.090958
AGELVL_B 1.151897
AGELVL_C 1.653567
AGELVL_E 1.400770
AGELVL_F 1.277023
AGELVL_G 1.262300
AGELVL_H 1.354998
AGELVL_I 1.508727
LOC_01 1.019717
LOC_04 1.046466
LOC_06 1.073594
LOC_17 1.024308
LOC_20 1.010226
LOC_24 1.114790
LOC_29 1.021318
LOC_30 1.017084
LOC_35 1.037466
LOC_39 1.027712
LOC_42 1.029216
LOC_46 1.017137
LOC_48 1.077366
LOC_51 1.103727
LOC_53 1.036590
TOA_20 1.014292
TOA_30 1.182450
TOA_35 1.053153
TOA_38 1.065029
TOA_42 1.006464
TOA_44 1.002625
PPGROUP_11 1.136123
Removed AFTER this step: LOC_01
Call:
glm(formula = fla, family = "binomial", data = OPMAnalysisDataNoFamBinary)
Deviance Residuals:
Min 1Q Median 3Q Max
-2.8357 -0.7426 -0.1396 0.8210 3.1864
Coefficients:
Estimate Std. Error z value Pr(>|z|)
(Intercept) 2.404722 0.314060 7.657 1.90e-14 ***
GSEGRD -0.070372 0.014131 -4.980 6.36e-07 ***
LowerLimitAge -0.044455 0.002602 -17.082 < 2e-16 ***
BLS_FEDERAL_OtherSep_Rate 3.473632 0.267317 12.994 < 2e-16 ***
BLS_FEDERAL_JobOpenings_Level 0.017480 0.001840 9.502 < 2e-16 ***
LOSSqrt -0.788198 0.019948 -39.513 < 2e-16 ***
SEPCount_EFDATE_OCCLog -0.030964 0.015958 -1.940 0.052331 .
AGELVL_B1 -0.566431 0.217699 -2.602 0.009271 **
AGELVL_C1 -0.175700 0.084973 -2.068 0.038667 *
AGELVL_E1 0.282576 0.067091 4.212 2.53e-05 ***
AGELVL_F1 0.499843 0.069814 7.160 8.09e-13 ***
AGELVL_G1 0.724842 0.073691 9.836 < 2e-16 ***
AGELVL_H1 0.856697 0.075970 11.277 < 2e-16 ***
AGELVL_I1 0.723699 0.080935 8.942 < 2e-16 ***
LOC_041 0.500660 0.131854 3.797 0.000146 ***
LOC_061 0.394184 0.081180 4.856 1.20e-06 ***
LOC_171 -0.244415 0.140275 -1.742 0.081439 .
LOC_201 0.594707 0.252563 2.355 0.018538 *
LOC_241 -0.129963 0.077038 -1.687 0.091603 .
LOC_291 -0.379545 0.164794 -2.303 0.021270 *
LOC_301 0.647414 0.236797 2.734 0.006256 **
LOC_351 0.443536 0.146225 3.033 0.002419 **
LOC_391 -0.309232 0.138084 -2.239 0.025127 *
LOC_421 -0.320052 0.139352 -2.297 0.021634 *
LOC_461 0.785760 0.250486 3.137 0.001707 **
LOC_481 0.227017 0.086174 2.634 0.008429 **
LOC_511 -0.173676 0.071855 -2.417 0.015647 *
LOC_531 0.376442 0.123363 3.052 0.002277 **
TOA_201 1.107347 0.191649 5.778 7.56e-09 ***
TOA_301 0.339791 0.086900 3.910 9.22e-05 ***
TOA_351 -0.592705 0.283301 -2.092 0.036427 *
TOA_381 0.304139 0.063998 4.752 2.01e-06 ***
TOA_421 1.127037 0.440279 2.560 0.010473 *
TOA_441 2.143992 1.071237 2.001 0.045347 *
PPGROUP_111 -0.430148 0.115913 -3.711 0.000206 ***
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
(Dispersion parameter for binomial family taken to be 1)
Null deviance: 20683 on 14919 degrees of freedom
Residual deviance: 14568 on 14885 degrees of freedom
AIC: 14638
Number of Fisher Scoring iterations: 5
VIF
GSEGRD 1.435790
LowerLimitAge 2.228846
BLS_FEDERAL_OtherSep_Rate 1.118045
BLS_FEDERAL_JobOpenings_Level 1.080979
LOSSqrt 1.411164
SEPCount_EFDATE_OCCLog 1.091001
AGELVL_B 1.152035
AGELVL_C 1.653633
AGELVL_E 1.400446
AGELVL_F 1.276537
AGELVL_G 1.262359
AGELVL_H 1.355196
AGELVL_I 1.509271
LOC_04 1.044765
LOC_06 1.068700
LOC_17 1.022750
LOC_20 1.009747
LOC_24 1.109539
LOC_29 1.020207
LOC_30 1.016531
LOC_35 1.035993
LOC_39 1.026006
LOC_42 1.027542
LOC_46 1.016655
LOC_48 1.073172
LOC_51 1.097751
LOC_53 1.034479
TOA_20 1.014194
TOA_30 1.181889
TOA_35 1.053121
TOA_38 1.064879
TOA_42 1.006467
TOA_44 1.002604
PPGROUP_11 1.135982
Removed AFTER this step: LOC_24
Call:
glm(formula = fla, family = "binomial", data = OPMAnalysisDataNoFamBinary)
Deviance Residuals:
Min 1Q Median 3Q Max
-2.8398 -0.7438 -0.1399 0.8201 3.1933
Coefficients:
Estimate Std. Error z value Pr(>|z|)
(Intercept) 2.397151 0.314022 7.634 2.28e-14 ***
GSEGRD -0.073329 0.014025 -5.229 1.71e-07 ***
LowerLimitAge -0.044388 0.002602 -17.062 < 2e-16 ***
BLS_FEDERAL_OtherSep_Rate 3.478798 0.267266 13.016 < 2e-16 ***
BLS_FEDERAL_JobOpenings_Level 0.017506 0.001839 9.518 < 2e-16 ***
LOSSqrt -0.786901 0.019927 -39.490 < 2e-16 ***
SEPCount_EFDATE_OCCLog -0.030636 0.015954 -1.920 0.054827 .
AGELVL_B1 -0.568014 0.217795 -2.608 0.009107 **
AGELVL_C1 -0.177206 0.084965 -2.086 0.037012 *
AGELVL_E1 0.283270 0.067092 4.222 2.42e-05 ***
AGELVL_F1 0.499108 0.069817 7.149 8.75e-13 ***
AGELVL_G1 0.724958 0.073668 9.841 < 2e-16 ***
AGELVL_H1 0.856695 0.075940 11.281 < 2e-16 ***
AGELVL_I1 0.722119 0.080897 8.926 < 2e-16 ***
LOC_041 0.518626 0.131412 3.947 7.93e-05 ***
LOC_061 0.411642 0.080519 5.112 3.18e-07 ***
LOC_171 -0.226480 0.139892 -1.619 0.105455
LOC_201 0.612054 0.252344 2.425 0.015288 *
LOC_291 -0.363176 0.164536 -2.207 0.027295 *
LOC_301 0.663652 0.236583 2.805 0.005029 **
LOC_351 0.461066 0.145844 3.161 0.001570 **
LOC_391 -0.291498 0.137688 -2.117 0.034252 *
LOC_421 -0.301898 0.138929 -2.173 0.029777 *
LOC_461 0.800962 0.250341 3.199 0.001377 **
LOC_481 0.245487 0.085467 2.872 0.004075 **
LOC_511 -0.153810 0.070889 -2.170 0.030027 *
LOC_531 0.394527 0.122875 3.211 0.001324 **
TOA_201 1.111242 0.191572 5.801 6.61e-09 ***
TOA_301 0.354148 0.086505 4.094 4.24e-05 ***
TOA_351 -0.606255 0.282982 -2.142 0.032163 *
TOA_381 0.312748 0.063780 4.904 9.41e-07 ***
TOA_421 1.124635 0.440332 2.554 0.010647 *
TOA_441 2.172956 1.071076 2.029 0.042483 *
PPGROUP_111 -0.418824 0.115586 -3.623 0.000291 ***
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
(Dispersion parameter for binomial family taken to be 1)
Null deviance: 20683 on 14919 degrees of freedom
Residual deviance: 14571 on 14886 degrees of freedom
AIC: 14639
Number of Fisher Scoring iterations: 5
VIF
GSEGRD 1.414132
LowerLimitAge 2.228218
BLS_FEDERAL_OtherSep_Rate 1.118058
BLS_FEDERAL_JobOpenings_Level 1.081055
LOSSqrt 1.408028
SEPCount_EFDATE_OCCLog 1.090862
AGELVL_B 1.151972
AGELVL_C 1.653603
AGELVL_E 1.400482
AGELVL_F 1.276403
AGELVL_G 1.262428
AGELVL_H 1.355066
AGELVL_I 1.508723
LOC_04 1.037962
LOC_06 1.051265
LOC_17 1.016828
LOC_20 1.008066
LOC_29 1.016635
LOC_30 1.014882
LOC_35 1.030761
LOC_39 1.020027
LOC_42 1.021352
LOC_46 1.015328
LOC_48 1.055825
LOC_51 1.067927
LOC_53 1.026646
TOA_20 1.014077
TOA_30 1.170882
TOA_35 1.052610
TOA_38 1.057914
TOA_42 1.006489
TOA_44 1.002348
PPGROUP_11 1.132533
Removed AFTER this step: LOC_17
Call:
glm(formula = fla, family = "binomial", data = OPMAnalysisDataNoFamBinary)
Deviance Residuals:
Min 1Q Median 3Q Max
-2.8395 -0.7428 -0.1398 0.8209 3.1960
Coefficients:
Estimate Std. Error z value Pr(>|z|)
(Intercept) 2.381260 0.313801 7.588 3.24e-14 ***
GSEGRD -0.072869 0.014020 -5.197 2.02e-07 ***
LowerLimitAge -0.044319 0.002601 -17.041 < 2e-16 ***
BLS_FEDERAL_OtherSep_Rate 3.480776 0.267208 13.026 < 2e-16 ***
BLS_FEDERAL_JobOpenings_Level 0.017533 0.001839 9.534 < 2e-16 ***
LOSSqrt -0.787354 0.019925 -39.516 < 2e-16 ***
SEPCount_EFDATE_OCCLog -0.030891 0.015951 -1.937 0.052784 .
AGELVL_B1 -0.566213 0.217694 -2.601 0.009296 **
AGELVL_C1 -0.176287 0.084947 -2.075 0.037963 *
AGELVL_E1 0.284571 0.067075 4.243 2.21e-05 ***
AGELVL_F1 0.501685 0.069796 7.188 6.58e-13 ***
AGELVL_G1 0.726202 0.073662 9.859 < 2e-16 ***
AGELVL_H1 0.856103 0.075935 11.274 < 2e-16 ***
AGELVL_I1 0.721935 0.080887 8.925 < 2e-16 ***
LOC_041 0.526966 0.131316 4.013 6.00e-05 ***
LOC_061 0.420088 0.080349 5.228 1.71e-07 ***
LOC_201 0.620633 0.252298 2.460 0.013897 *
LOC_291 -0.354516 0.164448 -2.156 0.031100 *
LOC_301 0.672284 0.236533 2.842 0.004480 **
LOC_351 0.469709 0.145746 3.223 0.001270 **
LOC_391 -0.283005 0.137589 -2.057 0.039697 *
LOC_421 -0.293330 0.138830 -2.113 0.034612 *
LOC_461 0.809726 0.250283 3.235 0.001215 **
LOC_481 0.253756 0.085316 2.974 0.002937 **
LOC_511 -0.145413 0.070693 -2.057 0.039689 *
LOC_531 0.403080 0.122762 3.283 0.001025 **
TOA_201 1.112386 0.191527 5.808 6.32e-09 ***
TOA_301 0.354352 0.086517 4.096 4.21e-05 ***
TOA_351 -0.607520 0.283087 -2.146 0.031869 *
TOA_381 0.312899 0.063797 4.905 9.36e-07 ***
TOA_421 1.126043 0.440364 2.557 0.010556 *
TOA_441 2.179502 1.071052 2.035 0.041859 *
PPGROUP_111 -0.420732 0.115605 -3.639 0.000273 ***
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
(Dispersion parameter for binomial family taken to be 1)
Null deviance: 20683 on 14919 degrees of freedom
Residual deviance: 14574 on 14887 degrees of freedom
AIC: 14640
Number of Fisher Scoring iterations: 5
VIF
GSEGRD 1.413475
LowerLimitAge 2.227446
BLS_FEDERAL_OtherSep_Rate 1.118018
BLS_FEDERAL_JobOpenings_Level 1.080933
LOSSqrt 1.407857
SEPCount_EFDATE_OCCLog 1.090707
AGELVL_B 1.152181
AGELVL_C 1.653078
AGELVL_E 1.400079
AGELVL_F 1.275591
AGELVL_G 1.262241
AGELVL_H 1.355127
AGELVL_I 1.508955
LOC_04 1.036355
LOC_06 1.046846
LOC_20 1.007621
LOC_29 1.015550
LOC_30 1.014367
LOC_35 1.029377
LOC_39 1.018538
LOC_42 1.019846
LOC_46 1.014850
LOC_48 1.052041
LOC_51 1.062111
LOC_53 1.024737
TOA_20 1.014067
TOA_30 1.170833
TOA_35 1.052718
TOA_38 1.057918
TOA_42 1.006482
TOA_44 1.002333
PPGROUP_11 1.132295
Removed AFTER this step: SEPCount_EFDATE_OCCLog
Following variables removed based on p-values (in order of removal):
[1] "TOA_45" "LOC_21"
[3] "LOC_09" "LOC_36"
[5] "LOC_54" "LOC_16"
[7] "LOC_23" "LOC_45"
[9] "LOC_12" "SalaryOverUnderIndAvg"
[11] "LOC_05" "LOC_10"
[13] "BLS_FEDERAL_Layoffs_Rate" "LOC_31"
[15] "LOC_33" "LOC_37"
[17] "LOC_26" "LOC_15"
[19] "LOC_41" "TOA_32"
[21] "LOC_28" "LOC_22"
[23] "TOA_40" "LOC_02"
[25] "LOC_19" "LOC_44"
[27] "LOC_50" "LOC_25"
[29] "LOC_32" "LOC_40"
[31] "LOC_08" "TOA_15"
[33] "LOC_27" "LOC_18"
[35] "LOC_47" "LOC_49"
[37] "LOC_34" "LOC_38"
[39] "LOC_13" "LOC_55"
[41] "BLS_FEDERAL_Quits_Rate" "LOC_01"
[43] "LOC_24" "LOC_17"
[45] "SEPCount_EFDATE_OCCLog"
Null Deviances (in order):
[1] "6140.09618413702" "6139.70530359788" "6139.68430874922" "6139.65854475106"
[5] "6139.63313569129" "6139.59532196834" "6139.53772461692" "6139.48366441678"
[9] "6139.39728155705" "6139.29830098624" "6139.1900882605" "6139.08688894165"
[13] "6138.98092947028" "6138.85674008882" "6138.72579420097" "6138.59742406333"
[17] "6138.47085903648" "6138.27554988296" "6138.0311195164" "6137.73506640671"
[21] "6137.4303765745" "6137.10477253365" "6136.75626463363" "6136.40051668325"
[25] "6136.01092268138" "6135.58921284231" "6135.16054261118" "6134.63328743168"
[29] "6134.04181569797" "6133.25422902175" "6132.31543711746" "6131.41750130868"
[33] "6130.31513080467" "6129.17306670617" "6127.97760516643" "6126.80371644396"
[37] "6125.66955201923" "6124.55307944912" "6122.89611809423" "6121.27266108516"
[41] "6119.17124816164" "6117.1107132246" "6114.87372224233" "6112.02966681198"
[45] "6109.40921243411"
Min value at iteration = 45
Diff Degrees of Freedom (in order):
[1] "76" "75" "74" "73" "72" "71" "70" "69" "68" "67" "66" "65" "64" "63" "62"
[16] "61" "60" "59" "58" "57" "56" "55" "54" "53" "52" "51" "50" "49" "48" "47"
[31] "46" "45" "44" "43" "42" "41" "40" "39" "38" "37" "36" "35" "34" "33" "32"
Min value at iteration = 45
Log Likelihoods (in order):
[1] "-7271.52432906914" "-7271.71976933871" "-7271.73026676304"
[4] "-7271.74314876212" "-7271.75585329201" "-7271.77476015348"
[7] "-7271.80355882919" "-7271.83058892926" "-7271.87378035912"
[10] "-7271.92327064453" "-7271.9773770074" "-7272.02897666682"
[13] "-7272.08195640251" "-7272.14405109324" "-7272.20952403716"
[16] "-7272.27370910599" "-7272.33699161941" "-7272.43464619617"
[19] "-7272.55686137945" "-7272.70488793429" "-7272.8572328504"
[22] "-7273.02003487083" "-7273.19428882084" "-7273.37216279602"
[25] "-7273.56695979696" "-7273.7778147165" "-7273.99214983206"
[28] "-7274.25577742181" "-7274.55151328866" "-7274.94530662677"
[31] "-7275.41470257892" "-7275.86367048331" "-7276.41485573531"
[34] "-7276.98588778457" "-7277.58361855443" "-7278.17056291567"
[37] "-7278.73764512804" "-7279.29588141309" "-7280.12436209054"
[40] "-7280.93609059507" "-7281.98679705683" "-7283.01706452535"
[43] "-7284.13556001649" "-7285.55758773166" "-7286.8678149206"
Min value at iteration = 45
AIC values (in order):
[1] "14697.0486581383" "14695.4395386774" "14693.4605335261" "14691.4862975242"
[5] "14689.511706584" "14687.549520307" "14685.6071176584" "14683.6611778585"
[9] "14681.7475607182" "14679.8465412891" "14677.9547540148" "14676.0579533336"
[13] "14674.163912805" "14672.2881021865" "14670.4190480743" "14668.547418212"
[17] "14666.6739832388" "14664.8692923923" "14663.1137227589" "14661.4097758686"
[21] "14659.7144657008" "14658.0400697417" "14656.3885776417" "14654.744325592"
[25] "14653.1339195939" "14651.555629433" "14649.9842996641" "14648.5115548436"
[29] "14647.1030265773" "14645.8906132535" "14644.8294051578" "14643.7273409666"
[33] "14642.8297114706" "14641.9717755691" "14641.1672371089" "14640.3411258313"
[37] "14639.4752902561" "14638.5917628262" "14638.2487241811" "14637.8721811901"
[41] "14637.9735941137" "14638.0341290507" "14638.271120033" "14639.1151754633"
[45] "14639.7356298412"
Min value at iteration = 40
BIC values (in order):
[1] "15283.0539144176" "15273.834337083" "15264.2448740579" "15254.6601801823"
[5] "15245.0751313683" "15235.5024872175" "15225.9496266952" "15216.3932290216"
[9] "15206.8691540075" "15197.3576767046" "15187.8554315566" "15178.3481730017"
[13] "15168.8436745993" "15159.357406107" "15149.8778941211" "15140.395806385"
[17] "15130.911913538" "15121.4967648178" "15112.1307373106" "15102.8163325465"
[21] "15093.510564505" "15084.2257106721" "15074.9637606983" "15065.709050775"
[25] "15056.4881869031" "15047.2994388684" "15038.1176512258" "15029.0344485315"
[29] "15020.0154623915" "15011.1925911939" "15002.5209252245" "14993.8084031595"
[33] "14985.3003157897" "14976.8319220145" "14968.4169256805" "14959.9803565292"
[37] "14951.5040630801" "14943.0100777765" "14935.0565812576" "14927.0695803929"
[41] "14919.5605354427" "14912.010612506" "14904.6371456145" "14897.8707431711"
[45] "14890.8807396752"
Min value at iteration = 45
%R LRSigCols <- varsP.Repeat
array(['GSEGRD', 'LowerLimitAge', 'BLS_FEDERAL_OtherSep_Rate',
'BLS_FEDERAL_JobOpenings_Level', 'LOSSqrt', 'AGELVL_B', 'AGELVL_C',
'AGELVL_E', 'AGELVL_F', 'AGELVL_G', 'AGELVL_H', 'AGELVL_I',
'LOC_04', 'LOC_06', 'LOC_20', 'LOC_29', 'LOC_30', 'LOC_35',
'LOC_39', 'LOC_42', 'LOC_46', 'LOC_48', 'LOC_51', 'LOC_53',
'TOA_20', 'TOA_30', 'TOA_35', 'TOA_38', 'TOA_42', 'TOA_44',
'PPGROUP_11'],
dtype='<U29')
%R -o LRSigCols
#from rpy2.robjects import pandas2ri
#pandas2ri.activate()
#LRSigCols
LRSigCols = pandas2ri.ri2py(LRSigCols)
LRSigCols = LRSigCols.tolist()
%%time
def lr_explorBinary( cost,
Data = OPMAnalysisDataNoFamBinary,
cols = PCList,
cv = cv,
seed = seed):
startTime = datetime.now()
y = Data["SEP"].values # get the labels we want
if ("SEP" in cols): X = Data[cols].drop("SEP", axis=1).as_matrix()
else: X = Data[cols]
lr_clf = LogisticRegression(penalty='l2', C=cost, class_weight=None, random_state=seed) # get object
# setup pipeline to take PCA, then fit a clf model
clf_pipe = Pipeline(
[('minMaxScaler',MinMaxScaler()),
('CLF',lr_clf)]
)
accuracy = cross_val_score(clf_pipe, X, y, cv=cv.split(X, y)) # this also can help with parallelism
MeanAccuracy = sum(accuracy)/len(accuracy)
accuracy = np.append(accuracy, MeanAccuracy)
endTime = datetime.now()
TotalTime = endTime - startTime
accuracy = np.append(accuracy, TotalTime)
return accuracy
CPU times: user 5 µs, sys: 0 ns, total: 5 µs Wall time: 11.7 µs
%%time
def lr_explorBinary_w_PCA(cost,
PCA,
Data = OPMAnalysisDataNoFamBinary,
cv = cv,
seed = seed):
startTime = datetime.now()
y = Data["SEP"].values # get the labels we want
X = Data.drop("SEP", axis=1).as_matrix()
lr_clf = LogisticRegression(penalty='l2', C=cost, class_weight=None, random_state=seed) # get object
# setup pipeline to take PCA, then fit a clf model
clf_pipe = Pipeline(
[('minMaxScaler',MinMaxScaler()),
('PCA',PCA),
('CLF',lr_clf)]
)
accuracy = cross_val_score(clf_pipe, X, y, cv=cv.split(X, y)) # this also can help with parallelism
MeanAccuracy = sum(accuracy)/len(accuracy)
accuracy = np.append(accuracy, MeanAccuracy)
endTime = datetime.now()
TotalTime = endTime - startTime
accuracy = np.append(accuracy, TotalTime)
return accuracy
CPU times: user 5 µs, sys: 0 ns, total: 5 µs Wall time: 9.06 µs
%%time
##Full Columns
acclist = []
cost = [.00000001, .0001, .001, .01, .05, 1.0, 2.0, 3.0, 4.0, 5.0]
for i in range(0,len(cost)):
acclist.append(lr_explorBinary(cost = cost[i],
cols = fullColumns))
LRdf = pd.DataFrame(pd.concat([pd.DataFrame({
"ModelVersion": "Logistic Regression: All Raw Features",
"Cost": cost
})[["ModelVersion", "Cost"]],
pd.DataFrame(acclist)], axis = 1).reindex())
LRdf.columns = ['ModelVersion','Cost', 'Iteration 0', 'Iteration 1', 'Iteration 2', 'Iteration 3', 'Iteration 4', 'MeanAccuracy', 'RunTime']
display(LRdf)
TopResultsDF = pd.concat([TopResultsDF, LRdf.sort_values(['MeanAccuracy'], ascending=False)[TopResultsDF.columns].head(1)]).sort_values(['MeanAccuracy'], ascending=False).reset_index(drop=True)
del LRdf, acclist
##Reduced Columns
acclist = []
cost = [.00000001, .0001, .001, .01, .05, 1.0, 2.0, 3.0, 4.0, 5.0]
for i in range(0,len(cost)):
acclist.append(lr_explorBinary(cost = cost[i]))
LRdf = pd.DataFrame(pd.concat([pd.DataFrame({
"ModelVersion": "Logistic Regression: Top 15 from PCA Raw Features",
"Cost": cost
})[["ModelVersion", "Cost"]],
pd.DataFrame(acclist)], axis = 1).reindex())
LRdf.columns = ['ModelVersion','Cost', 'Iteration 0', 'Iteration 1', 'Iteration 2', 'Iteration 3', 'Iteration 4', 'MeanAccuracy', 'RunTime']
display(LRdf)
TopResultsDF = pd.concat([TopResultsDF, LRdf.sort_values(['MeanAccuracy'], ascending=False)[TopResultsDF.columns].head(1)]).sort_values(['MeanAccuracy'], ascending=False).reset_index(drop=True)
del LRdf, acclist
##With PCA
acclist = []
cost = [.00000001, .0001, .001, .01, .05, 1.0, 2.0, 3.0, 4.0, 5.0]
for i in range(0,len(cost)):
acclist.append(lr_explorBinary_w_PCA(cost = cost[i],
PCA = PCA(n_components=23, svd_solver='randomized', random_state = seed)))
LRdf = pd.DataFrame(pd.concat([pd.DataFrame({
"ModelVersion": "Logistic Regression: With PCA",
"Cost": cost
})[["ModelVersion", "Cost"]],
pd.DataFrame(acclist)], axis = 1).reindex())
LRdf.columns = ['ModelVersion','Cost', 'Iteration 0', 'Iteration 1', 'Iteration 2', 'Iteration 3', 'Iteration 4', 'MeanAccuracy', 'RunTime']
display(LRdf)
TopResultsDF = pd.concat([TopResultsDF, LRdf.sort_values(['MeanAccuracy'], ascending=False)[TopResultsDF.columns].head(1)]).sort_values(['MeanAccuracy'], ascending=False).reset_index(drop=True)
del LRdf, acclist
##Significant Column List from Manual Tuning in R
acclist = []
cost = [.00000001, .0001, .001, .01, .05, 1.0, 2.0, 3.0, 4.0, 5.0]
for i in range(0,len(cost)):
acclist.append(lr_explorBinary(cost = cost[i],
cols = LRSigCols))
LRdf = pd.DataFrame(pd.concat([pd.DataFrame({
"ModelVersion": "Logistic Regression: Manual Significant Features",
"Cost": cost
})[["ModelVersion", "Cost"]],
pd.DataFrame(acclist)], axis = 1).reindex())
LRdf.columns = ['ModelVersion','Cost', 'Iteration 0', 'Iteration 1', 'Iteration 2', 'Iteration 3', 'Iteration 4', 'MeanAccuracy', 'RunTime']
display(LRdf)
TopResultsDF = pd.concat([TopResultsDF, LRdf.sort_values(['MeanAccuracy'], ascending=False)[TopResultsDF.columns].head(1)]).sort_values(['MeanAccuracy'], ascending=False).reset_index(drop=True)
del LRdf, acclist
| ModelVersion | Cost | Iteration 0 | Iteration 1 | Iteration 2 | Iteration 3 | Iteration 4 | MeanAccuracy | RunTime | |
|---|---|---|---|---|---|---|---|---|---|
| 0 | Logistic Regression: All Raw Features | 1.000000e-08 | 0.591290 | 0.676717 | 0.593499 | 0.619511 | 0.621857 | 0.620575 | 00:00:00.389469 |
| 1 | Logistic Regression: All Raw Features | 1.000000e-04 | 0.649246 | 0.582580 | 0.693700 | 0.698961 | 0.715052 | 0.667908 | 00:00:00.470639 |
| 2 | Logistic Regression: All Raw Features | 1.000000e-03 | 0.667002 | 0.565159 | 0.716823 | 0.725109 | 0.722762 | 0.679371 | 00:00:00.461081 |
| 3 | Logistic Regression: All Raw Features | 1.000000e-02 | 0.654271 | 0.562814 | 0.714477 | 0.741200 | 0.728797 | 0.680312 | 00:00:00.542275 |
| 4 | Logistic Regression: All Raw Features | 5.000000e-02 | 0.641541 | 0.572194 | 0.721180 | 0.754274 | 0.731814 | 0.684201 | 00:00:00.584583 |
| 5 | Logistic Regression: All Raw Features | 1.000000e+00 | 0.644556 | 0.672027 | 0.719169 | 0.749581 | 0.670466 | 0.691160 | 00:00:00.842935 |
| 6 | Logistic Regression: All Raw Features | 2.000000e+00 | 0.635176 | 0.730988 | 0.726206 | 0.748240 | 0.658062 | 0.699735 | 00:00:00.913229 |
| 7 | Logistic Regression: All Raw Features | 3.000000e+00 | 0.622446 | 0.763484 | 0.730563 | 0.750922 | 0.651693 | 0.703821 | 00:00:01.030300 |
| 8 | Logistic Regression: All Raw Features | 4.000000e+00 | 0.619095 | 0.781240 | 0.733914 | 0.752263 | 0.647335 | 0.706769 | 00:00:01.081118 |
| 9 | Logistic Regression: All Raw Features | 5.000000e+00 | 0.611725 | 0.790955 | 0.734584 | 0.755615 | 0.647000 | 0.707976 | 00:00:01.070989 |
| ModelVersion | Cost | Iteration 0 | Iteration 1 | Iteration 2 | Iteration 3 | Iteration 4 | MeanAccuracy | RunTime | |
|---|---|---|---|---|---|---|---|---|---|
| 0 | Logistic Regression: Top 15 from PCA Raw Features | 1.000000e-08 | 0.597320 | 0.678727 | 0.597855 | 0.624204 | 0.625880 | 0.624797 | 00:00:00.332468 |
| 1 | Logistic Regression: Top 15 from PCA Raw Features | 1.000000e-04 | 0.650921 | 0.582915 | 0.694705 | 0.699631 | 0.714381 | 0.668511 | 00:00:00.381781 |
| 2 | Logistic Regression: Top 15 from PCA Raw Features | 1.000000e-03 | 0.665327 | 0.564154 | 0.716488 | 0.725779 | 0.722762 | 0.678902 | 00:00:00.420797 |
| 3 | Logistic Regression: Top 15 from PCA Raw Features | 1.000000e-02 | 0.651591 | 0.563149 | 0.714812 | 0.742876 | 0.729802 | 0.680446 | 00:00:00.477449 |
| 4 | Logistic Regression: Top 15 from PCA Raw Features | 5.000000e-02 | 0.638861 | 0.569514 | 0.720509 | 0.752933 | 0.735501 | 0.683464 | 00:00:00.554424 |
| 5 | Logistic Regression: Top 15 from PCA Raw Features | 1.000000e+00 | 0.642881 | 0.681742 | 0.725201 | 0.750587 | 0.669460 | 0.693974 | 00:00:00.776193 |
| 6 | Logistic Regression: Top 15 from PCA Raw Features | 2.000000e+00 | 0.635846 | 0.744054 | 0.731568 | 0.751257 | 0.656051 | 0.703755 | 00:00:01.045872 |
| 7 | Logistic Regression: Top 15 from PCA Raw Features | 3.000000e+00 | 0.625796 | 0.781575 | 0.735590 | 0.752598 | 0.651022 | 0.709316 | 00:00:01.013846 |
| 8 | Logistic Regression: Top 15 from PCA Raw Features | 4.000000e+00 | 0.617755 | 0.793970 | 0.740282 | 0.755280 | 0.646664 | 0.710790 | 00:00:01.011212 |
| 9 | Logistic Regression: Top 15 from PCA Raw Features | 5.000000e+00 | 0.612060 | 0.796650 | 0.740617 | 0.755615 | 0.645323 | 0.710053 | 00:00:01.098474 |
| ModelVersion | Cost | Iteration 0 | Iteration 1 | Iteration 2 | Iteration 3 | Iteration 4 | MeanAccuracy | RunTime | |
|---|---|---|---|---|---|---|---|---|---|
| 0 | Logistic Regression: With PCA | 1.000000e-08 | 0.694472 | 0.637521 | 0.712466 | 0.728797 | 0.716058 | 0.697863 | 00:00:00.822884 |
| 1 | Logistic Regression: With PCA | 1.000000e-04 | 0.691457 | 0.617755 | 0.709786 | 0.729132 | 0.716728 | 0.692972 | 00:00:00.915029 |
| 2 | Logistic Regression: With PCA | 1.000000e-03 | 0.682077 | 0.567504 | 0.710791 | 0.733155 | 0.716058 | 0.681917 | 00:00:00.874856 |
| 3 | Logistic Regression: With PCA | 1.000000e-02 | 0.642211 | 0.557789 | 0.700737 | 0.731814 | 0.707342 | 0.667978 | 00:00:00.927182 |
| 4 | Logistic Regression: With PCA | 5.000000e-02 | 0.626466 | 0.554104 | 0.692694 | 0.735501 | 0.711700 | 0.664093 | 00:00:00.946569 |
| 5 | Logistic Regression: With PCA | 1.000000e+00 | 0.617755 | 0.551424 | 0.688673 | 0.736172 | 0.711029 | 0.661011 | 00:00:00.971852 |
| 6 | Logistic Regression: With PCA | 2.000000e+00 | 0.617085 | 0.551089 | 0.688673 | 0.736507 | 0.710359 | 0.660743 | 00:00:01.000370 |
| 7 | Logistic Regression: With PCA | 3.000000e+00 | 0.616750 | 0.551089 | 0.689008 | 0.736507 | 0.710023 | 0.660676 | 00:00:01.123128 |
| 8 | Logistic Regression: With PCA | 4.000000e+00 | 0.617085 | 0.551089 | 0.689008 | 0.736507 | 0.710359 | 0.660810 | 00:00:01.069807 |
| 9 | Logistic Regression: With PCA | 5.000000e+00 | 0.617085 | 0.551089 | 0.688673 | 0.736507 | 0.710359 | 0.660743 | 00:00:00.992059 |
| ModelVersion | Cost | Iteration 0 | Iteration 1 | Iteration 2 | Iteration 3 | Iteration 4 | MeanAccuracy | RunTime | |
|---|---|---|---|---|---|---|---|---|---|
| 0 | Logistic Regression: Manual Significant Features | 1.000000e-08 | 0.518928 | 0.544389 | 0.517426 | 0.521623 | 0.511901 | 0.522853 | 00:00:00.278712 |
| 1 | Logistic Regression: Manual Significant Features | 1.000000e-04 | 0.585595 | 0.641876 | 0.616287 | 0.609454 | 0.586658 | 0.607974 | 00:00:00.316484 |
| 2 | Logistic Regression: Manual Significant Features | 1.000000e-03 | 0.654941 | 0.737688 | 0.705094 | 0.703989 | 0.693597 | 0.699062 | 00:00:00.368627 |
| 3 | Logistic Regression: Manual Significant Features | 1.000000e-02 | 0.672362 | 0.782915 | 0.713137 | 0.740194 | 0.723433 | 0.726408 | 00:00:00.355737 |
| 4 | Logistic Regression: Manual Significant Features | 5.000000e-02 | 0.676382 | 0.795980 | 0.724531 | 0.753939 | 0.741200 | 0.738406 | 00:00:00.399969 |
| 5 | Logistic Regression: Manual Significant Features | 1.000000e+00 | 0.682077 | 0.802010 | 0.732239 | 0.757627 | 0.747905 | 0.744371 | 00:00:00.500998 |
| 6 | Logistic Regression: Manual Significant Features | 2.000000e+00 | 0.680737 | 0.801675 | 0.732909 | 0.757291 | 0.747570 | 0.744036 | 00:00:00.555597 |
| 7 | Logistic Regression: Manual Significant Features | 3.000000e+00 | 0.680737 | 0.801675 | 0.732574 | 0.756956 | 0.748240 | 0.744036 | 00:00:00.540252 |
| 8 | Logistic Regression: Manual Significant Features | 4.000000e+00 | 0.681072 | 0.801675 | 0.732574 | 0.756956 | 0.747905 | 0.744036 | 00:00:00.552915 |
| 9 | Logistic Regression: Manual Significant Features | 5.000000e+00 | 0.681072 | 0.801675 | 0.732909 | 0.756956 | 0.747905 | 0.744103 | 00:00:00.546188 |
CPU times: user 2min 39s, sys: 14min 46s, total: 17min 26s Wall time: 28.7 s
display(TopResultsDF)
plot = TopResultsDF[["Iteration 0","Iteration 1","Iteration 2","Iteration 3","Iteration 4"]].transpose().plot.line(title = "Top Results Among Varying Model Feature Inputs",rot=45)
plot.set_xlabel("Iterations")
plot.set_ylabel("Accuracies")
plot.legend(loc='center left', bbox_to_anchor=(1.01, .5))
FinalResultsDF = pd.concat([FinalResultsDF, TopResultsDF.sort_values(['MeanAccuracy'], ascending=False)[TopResultsDF.columns].head(1)]).sort_values(['MeanAccuracy'], ascending=False).reset_index(drop=True)
TopResultsDF = pd.DataFrame(columns= ['ModelVersion', 'Iteration 0', 'Iteration 1', 'Iteration 2', 'Iteration 3', 'Iteration 4', 'MeanAccuracy'])
| ModelVersion | Iteration 0 | Iteration 1 | Iteration 2 | Iteration 3 | Iteration 4 | MeanAccuracy | |
|---|---|---|---|---|---|---|---|
| 0 | Logistic Regression: Manual Significant Features | 0.682077 | 0.802010 | 0.732239 | 0.757627 | 0.747905 | 0.744371 |
| 1 | Logistic Regression: Top 15 from PCA Raw Features | 0.617755 | 0.793970 | 0.740282 | 0.755280 | 0.646664 | 0.710790 |
| 2 | Logistic Regression: All Raw Features | 0.611725 | 0.790955 | 0.734584 | 0.755615 | 0.647000 | 0.707976 |
| 3 | Logistic Regression: With PCA | 0.694472 | 0.637521 | 0.712466 | 0.728797 | 0.716058 | 0.697863 |
/usr/local/es7/lib/python3.5/site-packages/matplotlib/font_manager.py:1297: UserWarning: findfont: Font family ['sans-serif'] not found. Falling back to DejaVu Sans (prop.get_family(), self.defaultFamily[fontext]))
lr_clf = LogisticRegression(penalty='l2', C=1, class_weight=None, random_state=seed) # get object
lr_clf, lr_acc = compute_kfold_scores_ClassificationBinary(clf = lr_clf,
cols = LRSigCols)
/usr/local/es7/lib/python3.5/site-packages/matplotlib/font_manager.py:1297: UserWarning: findfont: Font family ['sans-serif'] not found. Falling back to DejaVu Sans (prop.get_family(), self.defaultFamily[fontext]))
confusion matrix Predicted NS SC All True NS 986 514 1500 SC 435 1050 1485 All 1421 1564 2985 Normalized confusion matrix [[ 0.65733333 0.34266667] [ 0.29292929 0.70707071]]
/usr/local/es7/lib/python3.5/site-packages/matplotlib/font_manager.py:1297: UserWarning: findfont: Font family ['sans-serif'] not found. Falling back to DejaVu Sans (prop.get_family(), self.defaultFamily[fontext]))
confusion matrix Predicted NS SC All True NS 1206 294 1500 SC 297 1188 1485 All 1503 1482 2985 Normalized confusion matrix [[ 0.804 0.196] [ 0.2 0.8 ]]
/usr/local/es7/lib/python3.5/site-packages/matplotlib/font_manager.py:1297: UserWarning: findfont: Font family ['sans-serif'] not found. Falling back to DejaVu Sans (prop.get_family(), self.defaultFamily[fontext]))
confusion matrix Predicted NS SC All True NS 815 684 1499 SC 115 1370 1485 All 930 2054 2984 Normalized confusion matrix [[ 0.5436958 0.4563042 ] [ 0.07744108 0.92255892]]
/usr/local/es7/lib/python3.5/site-packages/matplotlib/font_manager.py:1297: UserWarning: findfont: Font family ['sans-serif'] not found. Falling back to DejaVu Sans (prop.get_family(), self.defaultFamily[fontext]))
confusion matrix Predicted NS SC All True NS 1046 453 1499 SC 270 1214 1484 All 1316 1667 2983 Normalized confusion matrix [[ 0.69779853 0.30220147] [ 0.1819407 0.8180593 ]]
/usr/local/es7/lib/python3.5/site-packages/matplotlib/font_manager.py:1297: UserWarning: findfont: Font family ['sans-serif'] not found. Falling back to DejaVu Sans (prop.get_family(), self.defaultFamily[fontext]))
confusion matrix Predicted NS SC All True NS 1052 447 1499 SC 305 1179 1484 All 1357 1626 2983 Normalized confusion matrix [[ 0.7018012 0.2981988 ] [ 0.20552561 0.79447439]]
/usr/local/es7/lib/python3.5/site-packages/matplotlib/font_manager.py:1297: UserWarning: findfont: Font family ['sans-serif'] not found. Falling back to DejaVu Sans (prop.get_family(), self.defaultFamily[fontext]))
Accuracy Ratings across all iterations: [0.68208, 0.80201, 0.73224, 0.75763, 0.7479]
Average Accuracy: 0.74437
#### Random Forest Sign. Cols
acclist = []
n_estimators = [10 , 10 , 10 , 10 , 10 , 10 , 10 , 10 , 10 , 10 , 10 , 10 , 10 , 10 , 10 , 10 , 10 , 10 , 10 , 10 , 10 , 10 , 10 , 10 , 10 , 10 , 10 , 15 , 20 , 30 , 50 ]
max_features = ['auto', 'auto' , 'auto', 'auto', 'auto', 'auto', 'auto', 5 , 10 , 15 , 20 , None , 5 , 5 , 5 , 5 , 5 , 5 , 5 , 5 , 5 , 5 , 5 , 5 , 5 , 5 , 5 , 5 , 5 , 5 , 5 ]
max_depth = [None , None , None , None , None , None , None , None , None , None , None , None , 10 , 15 , 20 , 25 , 30 , 3 , 4 , 5 , 6 , 7 , 8 , 9 , 11 , 12 , 13 , 4 , 4 , 4 , 4 ]
min_samples_split = [2 , 8 , 12 , 18 , 20 , 24 , 36 , 18 , 18 , 18 , 18 , 18 , 18 , 18 , 18 , 18 , 18 , 18 , 18 , 18 , 18 , 18 , 18 , 18 , 18 , 18 , 18 , 18 , 18 , 18 , 18 ]
min_samples_leaf = [1 , 4 , 6 , 9 , 10 , 12 , 18 , 9 , 9 , 9 , 9 , 9 , 9 , 9 , 9 , 9 , 9 , 9 , 9 , 9 , 9 , 9 , 9 , 9 , 9 , 9 , 9 , 9 , 9 , 9 , 9 ]
## Model with only top 15 raw Scaled Principal Features
for i in range(0,len(n_estimators)):
acclist.append(rfc_explorBinary(n_estimators = n_estimators[i],
max_features = max_features[i],
max_depth = max_depth[i],
min_samples_split = min_samples_split[i],
min_samples_leaf = min_samples_leaf[i],
cols = LRSigCols
)
)
rfcdf = pd.DataFrame(pd.concat([pd.DataFrame({ "ModelVersion": "Random Forest: With LR Sig Cols",
"n_estimators": n_estimators,
"max_features": max_features,
"max_depth": max_depth,
"min_samples_split": min_samples_split,
"min_samples_leaf": min_samples_leaf
}),
pd.DataFrame(acclist)], axis = 1).reindex())
rfcdf.columns = ['ModelVersion', 'max_depth', 'max_features', 'min_samples_leaf','min_samples_split', 'n_estimators', 'Iteration 0', 'Iteration 1', 'Iteration 2', 'Iteration 3', 'Iteration 4', 'MeanAccuracy', 'RunTime']
display(rfcdf)
TopResultsDF = pd.concat([TopResultsDF, rfcdf.sort_values(['MeanAccuracy'], ascending=False)[TopResultsDF.columns].head(1)]).sort_values(['MeanAccuracy'], ascending=False).reset_index(drop=True)
del rfcdf, acclist
#### KNN Sign. Cols
acclist = []
n_neighbors = [5 , 10 , 15 , 20 , 30 , 40 , 50 , 100 , 150 , 200 , 250 , 200 , 200 , 200 , 200 , 200 , 200 , 200 , 200]
algorithm = 'ball_tree'
leaf_size = [30 , 30 , 30 , 30 , 30 , 30 , 30 , 30 , 30 , 30 , 30 , 2 , 3 , 4 , 5 , 20 , 50 , 100 , 150]
for i in range(0,len(n_neighbors)):
acclist.append(knn_explorBinary(n_neighbors = n_neighbors[i],
algorithm = algorithm,
leaf_size = leaf_size[i],
cols = LRSigCols
)
)
rfcdf = pd.DataFrame(pd.concat([pd.DataFrame({
"ModelVersion": "KNN: " + algorithm + ", With LR Sig Cols",
"n_neighbors": n_neighbors,
"algorithm": algorithm,
"leaf_size": leaf_size
}),
pd.DataFrame(acclist)], axis = 1).reindex())
rfcdf.columns = ['ModelVersion','algorithm', 'leaf_size','n_neighbors', 'Iteration 0', 'Iteration 1', 'Iteration 2', 'Iteration 3', 'Iteration 4', 'MeanAccuracy', 'RunTime']
display(rfcdf)
TopResultsDF = pd.concat([TopResultsDF, rfcdf.sort_values(['MeanAccuracy'], ascending=False)[TopResultsDF.columns].head(1)]).sort_values(['MeanAccuracy'], ascending=False).reset_index(drop=True)
del rfcdf, acclist
acclist = []
n_neighbors = [5 , 10 , 15 , 20 , 30 , 40 , 50 , 100 , 150 , 200 , 250 , 200 , 200 , 200 , 200 , 200 , 200 , 200 , 200]
algorithm = 'kd_tree'
leaf_size = [30 , 30 , 30 , 30 , 30 , 30 , 30 , 30 , 30 , 30 , 30 , 2 , 3 , 4 , 5 , 20 , 50 , 100 , 150]
for i in range(0,len(n_neighbors)):
acclist.append(knn_explorBinary(n_neighbors = n_neighbors[i],
algorithm = algorithm,
leaf_size = leaf_size[i],
cols = LRSigCols
)
)
rfcdf = pd.DataFrame(pd.concat([pd.DataFrame({
"ModelVersion": "KNN: " + algorithm + ", With LR Sig Cols",
"n_neighbors": n_neighbors,
"algorithm": algorithm,
"leaf_size": leaf_size
}),
pd.DataFrame(acclist)], axis = 1).reindex())
rfcdf.columns = ['ModelVersion','algorithm', 'leaf_size','n_neighbors', 'Iteration 0', 'Iteration 1', 'Iteration 2', 'Iteration 3', 'Iteration 4', 'MeanAccuracy', 'RunTime']
display(rfcdf)
TopResultsDF = pd.concat([TopResultsDF, rfcdf.sort_values(['MeanAccuracy'], ascending=False)[TopResultsDF.columns].head(1)]).sort_values(['MeanAccuracy'], ascending=False).reset_index(drop=True)
del rfcdf, acclist
| ModelVersion | max_depth | max_features | min_samples_leaf | min_samples_split | n_estimators | Iteration 0 | Iteration 1 | Iteration 2 | Iteration 3 | Iteration 4 | MeanAccuracy | RunTime | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | Random Forest: With LR Sig Cols | NaN | auto | 1 | 2 | 10 | 0.593300 | 0.696817 | 0.705429 | 0.732484 | 0.643983 | 0.674403 | 00:00:01.319429 |
| 1 | Random Forest: With LR Sig Cols | NaN | auto | 4 | 8 | 10 | 0.633836 | 0.721608 | 0.704424 | 0.739859 | 0.692927 | 0.698531 | 00:00:01.353345 |
| 2 | Random Forest: With LR Sig Cols | NaN | auto | 6 | 12 | 10 | 0.623451 | 0.728978 | 0.711461 | 0.748240 | 0.695944 | 0.701615 | 00:00:01.353523 |
| 3 | Random Forest: With LR Sig Cols | NaN | auto | 9 | 18 | 10 | 0.673367 | 0.733668 | 0.718499 | 0.743212 | 0.700637 | 0.713876 | 00:00:01.350717 |
| 4 | Random Forest: With LR Sig Cols | NaN | auto | 10 | 20 | 10 | 0.656281 | 0.728643 | 0.721515 | 0.733490 | 0.690580 | 0.706102 | 00:00:01.354176 |
| 5 | Random Forest: With LR Sig Cols | NaN | auto | 12 | 24 | 10 | 0.645896 | 0.722948 | 0.717828 | 0.736172 | 0.691586 | 0.702886 | 00:00:01.358586 |
| 6 | Random Forest: With LR Sig Cols | NaN | auto | 18 | 36 | 10 | 0.680067 | 0.724958 | 0.718834 | 0.736172 | 0.703654 | 0.712737 | 00:00:01.354258 |
| 7 | Random Forest: With LR Sig Cols | NaN | 5 | 9 | 18 | 10 | 0.673367 | 0.733668 | 0.718499 | 0.743212 | 0.700637 | 0.713876 | 00:00:01.355816 |
| 8 | Random Forest: With LR Sig Cols | NaN | 10 | 9 | 18 | 10 | 0.607370 | 0.718593 | 0.707775 | 0.746229 | 0.683875 | 0.692768 | 00:00:01.351966 |
| 9 | Random Forest: With LR Sig Cols | NaN | 15 | 9 | 18 | 10 | 0.609380 | 0.707873 | 0.718834 | 0.734831 | 0.683875 | 0.690959 | 00:00:01.354990 |
| 10 | Random Forest: With LR Sig Cols | NaN | 20 | 9 | 18 | 10 | 0.591625 | 0.707538 | 0.705094 | 0.733155 | 0.678176 | 0.683117 | 00:00:01.352300 |
| 11 | Random Forest: With LR Sig Cols | NaN | None | 9 | 18 | 10 | 0.607370 | 0.700503 | 0.702078 | 0.721086 | 0.678176 | 0.681843 | 00:00:01.353267 |
| 12 | Random Forest: With LR Sig Cols | 10.0 | 5 | 9 | 18 | 10 | 0.668677 | 0.738358 | 0.717493 | 0.743882 | 0.715722 | 0.716827 | 00:00:01.351438 |
| 13 | Random Forest: With LR Sig Cols | 15.0 | 5 | 9 | 18 | 10 | 0.644221 | 0.730988 | 0.707775 | 0.738518 | 0.704660 | 0.705232 | 00:00:01.347089 |
| 14 | Random Forest: With LR Sig Cols | 20.0 | 5 | 9 | 18 | 10 | 0.668342 | 0.727303 | 0.717158 | 0.731143 | 0.705665 | 0.709922 | 00:00:01.355219 |
| 15 | Random Forest: With LR Sig Cols | 25.0 | 5 | 9 | 18 | 10 | 0.673367 | 0.736013 | 0.717493 | 0.741871 | 0.700637 | 0.713876 | 00:00:01.355543 |
| 16 | Random Forest: With LR Sig Cols | 30.0 | 5 | 9 | 18 | 10 | 0.673367 | 0.733668 | 0.718499 | 0.743212 | 0.700637 | 0.713876 | 00:00:01.357082 |
| 17 | Random Forest: With LR Sig Cols | 3.0 | 5 | 9 | 18 | 10 | 0.705528 | 0.730653 | 0.718164 | 0.726785 | 0.701643 | 0.716554 | 00:00:01.356262 |
| 18 | Random Forest: With LR Sig Cols | 4.0 | 5 | 9 | 18 | 10 | 0.707203 | 0.747739 | 0.720509 | 0.745558 | 0.716058 | 0.727413 | 00:00:01.349467 |
| 19 | Random Forest: With LR Sig Cols | 5.0 | 5 | 9 | 18 | 10 | 0.697152 | 0.714908 | 0.709115 | 0.726115 | 0.705665 | 0.710591 | 00:00:01.324292 |
| 20 | Random Forest: With LR Sig Cols | 6.0 | 5 | 9 | 18 | 10 | 0.696482 | 0.732328 | 0.714477 | 0.738854 | 0.707006 | 0.717830 | 00:00:01.350889 |
| 21 | Random Forest: With LR Sig Cols | 7.0 | 5 | 9 | 18 | 10 | 0.667337 | 0.758124 | 0.711461 | 0.748240 | 0.720080 | 0.721048 | 00:00:01.591849 |
| 22 | Random Forest: With LR Sig Cols | 8.0 | 5 | 9 | 18 | 10 | 0.674372 | 0.744724 | 0.726206 | 0.739524 | 0.708683 | 0.718702 | 00:00:01.352953 |
| 23 | Random Forest: With LR Sig Cols | 9.0 | 5 | 9 | 18 | 10 | 0.686097 | 0.729648 | 0.714142 | 0.744552 | 0.719745 | 0.718837 | 00:00:01.353753 |
| 24 | Random Forest: With LR Sig Cols | 11.0 | 5 | 9 | 18 | 10 | 0.666332 | 0.738023 | 0.708780 | 0.744217 | 0.686222 | 0.708715 | 00:00:01.355513 |
| 25 | Random Forest: With LR Sig Cols | 12.0 | 5 | 9 | 18 | 10 | 0.664657 | 0.730988 | 0.720174 | 0.739189 | 0.683875 | 0.707777 | 00:00:01.380764 |
| 26 | Random Forest: With LR Sig Cols | 13.0 | 5 | 9 | 18 | 10 | 0.669682 | 0.728308 | 0.704088 | 0.741871 | 0.698961 | 0.708582 | 00:00:01.352695 |
| 27 | Random Forest: With LR Sig Cols | 4.0 | 5 | 9 | 18 | 15 | 0.703853 | 0.749079 | 0.717158 | 0.741200 | 0.710359 | 0.724330 | 00:00:01.387465 |
| 28 | Random Forest: With LR Sig Cols | 4.0 | 5 | 9 | 18 | 20 | 0.704523 | 0.711223 | 0.714142 | 0.742206 | 0.710694 | 0.716557 | 00:00:01.418469 |
| 29 | Random Forest: With LR Sig Cols | 4.0 | 5 | 9 | 18 | 30 | 0.710888 | 0.747739 | 0.721180 | 0.746899 | 0.714046 | 0.728150 | 00:00:01.485653 |
| 30 | Random Forest: With LR Sig Cols | 4.0 | 5 | 9 | 18 | 50 | 0.717253 | 0.752094 | 0.729223 | 0.752263 | 0.723098 | 0.734786 | 00:00:01.598768 |
| ModelVersion | algorithm | leaf_size | n_neighbors | Iteration 0 | Iteration 1 | Iteration 2 | Iteration 3 | Iteration 4 | MeanAccuracy | RunTime | |
|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | KNN: ball_tree, With LR Sig Cols | ball_tree | 30 | 5 | 0.603350 | 0.719598 | 0.682976 | 0.698290 | 0.636943 | 0.668231 | 00:00:09.339261 |
| 1 | KNN: ball_tree, With LR Sig Cols | ball_tree | 30 | 10 | 0.590955 | 0.708208 | 0.695710 | 0.708347 | 0.632920 | 0.667228 | 00:00:07.951656 |
| 2 | KNN: ball_tree, With LR Sig Cols | ball_tree | 30 | 15 | 0.610720 | 0.722613 | 0.689678 | 0.714717 | 0.631914 | 0.673929 | 00:00:09.513405 |
| 3 | KNN: ball_tree, With LR Sig Cols | ball_tree | 30 | 20 | 0.608375 | 0.722948 | 0.688673 | 0.720751 | 0.635267 | 0.675203 | 00:00:07.704102 |
| 4 | KNN: ball_tree, With LR Sig Cols | ball_tree | 30 | 30 | 0.610050 | 0.732998 | 0.690349 | 0.715052 | 0.646329 | 0.678956 | 00:00:10.493881 |
| 5 | KNN: ball_tree, With LR Sig Cols | ball_tree | 30 | 40 | 0.620436 | 0.730318 | 0.685992 | 0.714717 | 0.643983 | 0.679089 | 00:00:10.679234 |
| 6 | KNN: ball_tree, With LR Sig Cols | ball_tree | 30 | 50 | 0.627806 | 0.728308 | 0.680295 | 0.716058 | 0.648341 | 0.680161 | 00:00:10.780798 |
| 7 | KNN: ball_tree, With LR Sig Cols | ball_tree | 30 | 100 | 0.648576 | 0.694472 | 0.705429 | 0.727120 | 0.672477 | 0.689615 | 00:00:10.391326 |
| 8 | KNN: ball_tree, With LR Sig Cols | ball_tree | 30 | 150 | 0.649246 | 0.698157 | 0.710791 | 0.728126 | 0.679182 | 0.693101 | 00:00:09.293482 |
| 9 | KNN: ball_tree, With LR Sig Cols | ball_tree | 30 | 200 | 0.655611 | 0.702513 | 0.705094 | 0.718404 | 0.691921 | 0.694709 | 00:00:08.481927 |
| 10 | KNN: ball_tree, With LR Sig Cols | ball_tree | 30 | 250 | 0.664657 | 0.706533 | 0.700737 | 0.712705 | 0.686557 | 0.694238 | 00:00:09.507574 |
| 11 | KNN: ball_tree, With LR Sig Cols | ball_tree | 2 | 200 | 0.655611 | 0.702513 | 0.705094 | 0.718404 | 0.691921 | 0.694709 | 00:00:19.387612 |
| 12 | KNN: ball_tree, With LR Sig Cols | ball_tree | 3 | 200 | 0.655611 | 0.702513 | 0.705094 | 0.718404 | 0.691921 | 0.694709 | 00:00:16.680770 |
| 13 | KNN: ball_tree, With LR Sig Cols | ball_tree | 4 | 200 | 0.655611 | 0.702513 | 0.705094 | 0.718404 | 0.691921 | 0.694709 | 00:00:15.178915 |
| 14 | KNN: ball_tree, With LR Sig Cols | ball_tree | 5 | 200 | 0.655611 | 0.702513 | 0.705094 | 0.718404 | 0.691921 | 0.694709 | 00:00:17.365772 |
| 15 | KNN: ball_tree, With LR Sig Cols | ball_tree | 20 | 200 | 0.655611 | 0.702513 | 0.704759 | 0.718404 | 0.691921 | 0.694642 | 00:00:08.894266 |
| 16 | KNN: ball_tree, With LR Sig Cols | ball_tree | 50 | 200 | 0.655611 | 0.702513 | 0.705094 | 0.718404 | 0.691921 | 0.694709 | 00:00:07.524411 |
| 17 | KNN: ball_tree, With LR Sig Cols | ball_tree | 100 | 200 | 0.655611 | 0.702848 | 0.705094 | 0.718404 | 0.691921 | 0.694776 | 00:00:09.780008 |
| 18 | KNN: ball_tree, With LR Sig Cols | ball_tree | 150 | 200 | 0.655611 | 0.702848 | 0.705094 | 0.718404 | 0.691921 | 0.694776 | 00:00:09.699784 |
| ModelVersion | algorithm | leaf_size | n_neighbors | Iteration 0 | Iteration 1 | Iteration 2 | Iteration 3 | Iteration 4 | MeanAccuracy | RunTime | |
|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | KNN: kd_tree, With LR Sig Cols | kd_tree | 30 | 5 | 0.603350 | 0.719598 | 0.682976 | 0.698290 | 0.636943 | 0.668231 | 00:00:03.250072 |
| 1 | KNN: kd_tree, With LR Sig Cols | kd_tree | 30 | 10 | 0.592295 | 0.708208 | 0.693700 | 0.708347 | 0.633255 | 0.667161 | 00:00:02.927927 |
| 2 | KNN: kd_tree, With LR Sig Cols | kd_tree | 30 | 15 | 0.610385 | 0.722613 | 0.690349 | 0.714717 | 0.630908 | 0.673794 | 00:00:03.573363 |
| 3 | KNN: kd_tree, With LR Sig Cols | kd_tree | 30 | 20 | 0.608710 | 0.722948 | 0.688673 | 0.720416 | 0.634261 | 0.675002 | 00:00:03.364107 |
| 4 | KNN: kd_tree, With LR Sig Cols | kd_tree | 30 | 30 | 0.610720 | 0.732998 | 0.690684 | 0.715052 | 0.646329 | 0.679157 | 00:00:03.619864 |
| 5 | KNN: kd_tree, With LR Sig Cols | kd_tree | 30 | 40 | 0.620436 | 0.730318 | 0.685992 | 0.714717 | 0.643983 | 0.679089 | 00:00:05.258614 |
| 6 | KNN: kd_tree, With LR Sig Cols | kd_tree | 30 | 50 | 0.627471 | 0.728643 | 0.679960 | 0.716058 | 0.648005 | 0.680027 | 00:00:04.890978 |
| 7 | KNN: kd_tree, With LR Sig Cols | kd_tree | 30 | 100 | 0.648911 | 0.694472 | 0.705094 | 0.727456 | 0.672477 | 0.689682 | 00:00:06.706199 |
| 8 | KNN: kd_tree, With LR Sig Cols | kd_tree | 30 | 150 | 0.649246 | 0.698157 | 0.710791 | 0.727791 | 0.679182 | 0.693033 | 00:00:06.081361 |
| 9 | KNN: kd_tree, With LR Sig Cols | kd_tree | 30 | 200 | 0.655611 | 0.702513 | 0.704759 | 0.718404 | 0.691921 | 0.694642 | 00:00:06.672064 |
| 10 | KNN: kd_tree, With LR Sig Cols | kd_tree | 30 | 250 | 0.664657 | 0.706198 | 0.700402 | 0.712705 | 0.686557 | 0.694104 | 00:00:07.118846 |
| 11 | KNN: kd_tree, With LR Sig Cols | kd_tree | 2 | 200 | 0.655611 | 0.702513 | 0.705094 | 0.718404 | 0.691921 | 0.694709 | 00:00:08.801126 |
| 12 | KNN: kd_tree, With LR Sig Cols | kd_tree | 3 | 200 | 0.655611 | 0.702513 | 0.705094 | 0.718404 | 0.691921 | 0.694709 | 00:00:09.048474 |
| 13 | KNN: kd_tree, With LR Sig Cols | kd_tree | 4 | 200 | 0.655611 | 0.702513 | 0.705094 | 0.718404 | 0.691921 | 0.694709 | 00:00:08.715490 |
| 14 | KNN: kd_tree, With LR Sig Cols | kd_tree | 5 | 200 | 0.655611 | 0.702513 | 0.705094 | 0.718404 | 0.691921 | 0.694709 | 00:00:08.221623 |
| 15 | KNN: kd_tree, With LR Sig Cols | kd_tree | 20 | 200 | 0.655611 | 0.702513 | 0.705094 | 0.718404 | 0.691921 | 0.694709 | 00:00:06.788569 |
| 16 | KNN: kd_tree, With LR Sig Cols | kd_tree | 50 | 200 | 0.655611 | 0.702513 | 0.705094 | 0.718404 | 0.691921 | 0.694709 | 00:00:06.222049 |
| 17 | KNN: kd_tree, With LR Sig Cols | kd_tree | 100 | 200 | 0.655611 | 0.702513 | 0.705094 | 0.718404 | 0.691921 | 0.694709 | 00:00:07.252555 |
| 18 | KNN: kd_tree, With LR Sig Cols | kd_tree | 150 | 200 | 0.655611 | 0.702513 | 0.705094 | 0.718404 | 0.691921 | 0.694709 | 00:00:06.351647 |
display(TopResultsDF)
plot = TopResultsDF[["Iteration 0","Iteration 1","Iteration 2","Iteration 3","Iteration 4"]].transpose().plot.line(title = "Top Results Among Varying Model Feature Inputs",rot=45)
plot.set_xlabel("Iterations")
plot.set_ylabel("Accuracies")
plot.legend(loc='center left', bbox_to_anchor=(1.01, .5))
FinalResultsDF = pd.concat([FinalResultsDF, TopResultsDF.sort_values(['MeanAccuracy'], ascending=False)[TopResultsDF.columns].head(1)]).sort_values(['MeanAccuracy'], ascending=False).reset_index(drop=True)
TopResultsDF = pd.DataFrame(columns= ['ModelVersion', 'Iteration 0', 'Iteration 1', 'Iteration 2', 'Iteration 3', 'Iteration 4', 'MeanAccuracy'])
| ModelVersion | Iteration 0 | Iteration 1 | Iteration 2 | Iteration 3 | Iteration 4 | MeanAccuracy | |
|---|---|---|---|---|---|---|---|
| 0 | Random Forest: With LR Sig Cols | 0.717253 | 0.752094 | 0.729223 | 0.752263 | 0.723098 | 0.734786 |
| 1 | KNN: ball_tree, With LR Sig Cols | 0.655611 | 0.702848 | 0.705094 | 0.718404 | 0.691921 | 0.694776 |
| 2 | KNN: kd_tree, With LR Sig Cols | 0.655611 | 0.702513 | 0.705094 | 0.718404 | 0.691921 | 0.694709 |
/usr/local/es7/lib/python3.5/site-packages/matplotlib/font_manager.py:1297: UserWarning: findfont: Font family ['sans-serif'] not found. Falling back to DejaVu Sans (prop.get_family(), self.defaultFamily[fontext]))
%%time
rfc_clf = RandomForestClassifier(n_estimators =50,
max_features = 5,
max_depth = 4.0,
min_samples_split = 18,
min_samples_leaf = 9,
n_jobs = -1,
random_state = seed) # get object
rfc_clf, rfc_acc = compute_kfold_scores_ClassificationBinary(rfc_clf,
##PCA = PCA(n_components=22, svd_solver='randomized', random_state = seed),
cols = LRSigCols)
/usr/local/es7/lib/python3.5/site-packages/matplotlib/font_manager.py:1297: UserWarning: findfont: Font family ['sans-serif'] not found. Falling back to DejaVu Sans (prop.get_family(), self.defaultFamily[fontext]))
confusion matrix Predicted NS SC All True NS 871 629 1500 SC 215 1270 1485 All 1086 1899 2985 Normalized confusion matrix [[ 0.58066667 0.41933333] [ 0.14478114 0.85521886]]
/usr/local/es7/lib/python3.5/site-packages/matplotlib/font_manager.py:1297: UserWarning: findfont: Font family ['sans-serif'] not found. Falling back to DejaVu Sans (prop.get_family(), self.defaultFamily[fontext]))
confusion matrix Predicted NS SC All True NS 1296 204 1500 SC 536 949 1485 All 1832 1153 2985 Normalized confusion matrix [[ 0.864 0.136 ] [ 0.36094276 0.63905724]]
/usr/local/es7/lib/python3.5/site-packages/matplotlib/font_manager.py:1297: UserWarning: findfont: Font family ['sans-serif'] not found. Falling back to DejaVu Sans (prop.get_family(), self.defaultFamily[fontext]))
confusion matrix Predicted NS SC All True NS 838 661 1499 SC 147 1338 1485 All 985 1999 2984 Normalized confusion matrix [[ 0.55903936 0.44096064] [ 0.0989899 0.9010101 ]]
/usr/local/es7/lib/python3.5/site-packages/matplotlib/font_manager.py:1297: UserWarning: findfont: Font family ['sans-serif'] not found. Falling back to DejaVu Sans (prop.get_family(), self.defaultFamily[fontext]))
confusion matrix Predicted NS SC All True NS 951 548 1499 SC 191 1293 1484 All 1142 1841 2983 Normalized confusion matrix [[ 0.63442295 0.36557705] [ 0.1287062 0.8712938 ]]
/usr/local/es7/lib/python3.5/site-packages/matplotlib/font_manager.py:1297: UserWarning: findfont: Font family ['sans-serif'] not found. Falling back to DejaVu Sans (prop.get_family(), self.defaultFamily[fontext]))
confusion matrix Predicted NS SC All True NS 901 598 1499 SC 228 1256 1484 All 1129 1854 2983 Normalized confusion matrix [[ 0.60106738 0.39893262] [ 0.15363881 0.84636119]]
/usr/local/es7/lib/python3.5/site-packages/matplotlib/font_manager.py:1297: UserWarning: findfont: Font family ['sans-serif'] not found. Falling back to DejaVu Sans (prop.get_family(), self.defaultFamily[fontext]))
Accuracy Ratings across all iterations: [0.71725, 0.75209, 0.72922, 0.75226, 0.7231]
Average Accuracy: 0.73478
CPU times: user 8.83 s, sys: 2.77 s, total: 11.6 s
Wall time: 6.28 s
%%time
import os
from sklearn import tree
import pydotplus
import six
from sklearn.tree import export_graphviz
from IPython.display import SVG
i_tree = 0
for tree_in_forest in rfc_clf.estimators_:
svgData = tree.export_graphviz(tree_in_forest,
feature_names=fullColumns,
class_names=["NS", "SD"],
filled=True,
#rounded=True,
rotate = True,
label = 'All',
out_file=None)
graph=pydotplus.graph_from_dot_data(svgData)
if not os.path.exists('images'):
os.makedirs('images')
graph.write_svg('images/tree'+ str(i_tree) +'.svg')
i_tree = i_tree + 1
CPU times: user 11.7 s, sys: 1.69 s, total: 13.3 s Wall time: 16.6 s
SVG(filename='images/tree0.svg')
display(FinalResultsDF)
#plot top results across all model tests
plot = FinalResultsDF[["Iteration 0","Iteration 1","Iteration 2","Iteration 3","Iteration 4"]].transpose().plot.line(title = "Final Results across Model Types",rot=45)
plot.set_xlabel("Iterations")
plot.set_ylabel("Accuracies")
plot.legend(loc='center left', bbox_to_anchor=(1.01, .5))
del FinalResultsDF
| ModelVersion | Iteration 0 | Iteration 1 | Iteration 2 | Iteration 3 | Iteration 4 | MeanAccuracy | |
|---|---|---|---|---|---|---|---|
| 0 | Logistic Regression: Manual Significant Features | 0.682077 | 0.802010 | 0.732239 | 0.757627 | 0.747905 | 0.744371 |
| 1 | Random Forest: With LR Sig Cols | 0.717253 | 0.752094 | 0.729223 | 0.752263 | 0.723098 | 0.734786 |
| 2 | KNN: kd_tree, Full Raw Columns | 0.695477 | 0.762814 | 0.706099 | 0.741871 | 0.725779 | 0.726408 |
| 3 | Random Forest: Top 15 Raw from PC | 0.639866 | 0.723953 | 0.724531 | 0.762320 | 0.743882 | 0.718910 |
/usr/local/es7/lib/python3.5/site-packages/matplotlib/font_manager.py:1297: UserWarning: findfont: Font family ['sans-serif'] not found. Falling back to DejaVu Sans (prop.get_family(), self.defaultFamily[fontext]))
print(lr_clf.coef_[0])
coef = pd.Series(lr_clf.coef_[0], index=LRSigCols)
maxcoef = pd.Series(pd.DataFrame(abs(coef).sort_values(ascending=False).head(20)).index)
weightsplot = pd.Series(coef, index=maxcoef)
weightsplot.plot(title = "Logistic Regression Coefficients", kind='bar', color = 'Tomato')
[-0.53761554 -2.0345517 0.76432449 0.39580152 -6.3888843 -0.48416436 -0.12829652 0.31087776 0.51766391 0.74976631 0.86371825 0.67961665 0.55969504 0.4690265 0.44315626 -0.39532162 0.69560419 0.28090379 -0.20428945 -0.26813639 0.80055387 0.29524038 -0.07782224 0.37876725 0.84198517 0.2849319 -0.60110599 0.35630518 1.09238682 1.08448602 -0.46266585]
<matplotlib.axes._subplots.AxesSubplot at 0x7f7227bebb00>
/usr/local/es7/lib/python3.5/site-packages/matplotlib/font_manager.py:1297: UserWarning: findfont: Font family ['sans-serif'] not found. Falling back to DejaVu Sans (prop.get_family(), self.defaultFamily[fontext]))
Using the full datset, to create our model fit allows us to fully utilize our dataset instead of simply utilizing the last 80% training fold fit on external data.
y = OPMAnalysisDataNoFamBinary["SEP"].values # get the labels we want
y = np.where(y=="NS",0,1) # turn into numeric binary
X = OPMAnalysisDataNoFamBinary.drop("SEP", axis=1)
XFC = pd.DataFrame(OPMAnalysisScalerFit.transform(X),columns=X.columns)[fullColumns].as_matrix()
XPCC = pd.DataFrame(OPMAnalysisScalerFit.transform(X),columns=X.columns)[PCList]#.as_matrix()
XSigC = pd.DataFrame(OPMAnalysisScalerFit.transform(X),columns=X.columns)[LRSigCols].as_matrix()
rfc_clf = RandomForestClassifier(n_estimators =50,
max_features = 5,
max_depth = 4.0,
min_samples_split = 18,
min_samples_leaf = 9,
n_jobs = -1,
random_state = seed) # get object
rfc_clf.fit(XSigC,y)
RandomForestClassifier(bootstrap=True, class_weight=None, criterion='gini',
max_depth=4.0, max_features=5, max_leaf_nodes=None,
min_impurity_split=1e-07, min_samples_leaf=9,
min_samples_split=18, min_weight_fraction_leaf=0.0,
n_estimators=50, n_jobs=-1, oob_score=False,
random_state=14920, verbose=0, warm_start=False)
knn_clf = KNeighborsClassifier(n_neighbors = 250, algorithm = 'kd_tree',leaf_size = 30, n_jobs=-1) # get object
knn_clf.fit(XPCC,y)
KNeighborsClassifier(algorithm='kd_tree', leaf_size=30, metric='minkowski',
metric_params=None, n_jobs=-1, n_neighbors=250, p=2,
weights='uniform')
lr_clf = LogisticRegression(penalty='l2', C=1, class_weight=None, random_state=seed) # get object
lr_clf.fit(XSigC,y)
LogisticRegression(C=1, class_weight=None, dual=False, fit_intercept=True,
intercept_scaling=1, max_iter=100, multi_class='ovr', n_jobs=1,
penalty='l2', random_state=14920, solver='liblinear', tol=0.0001,
verbose=0, warm_start=False)
%%time
if os.path.isfile(PickleJarPath+"/OPMAnalysisDataNoFamAdminBinary.pkl"):
print("Found the File! Loading Pickle Now!")
OPMAnalysisDataNoFamAdminBinary = unpickleObject("OPMAnalysisDataNoFamAdminBinary")
else:
OPMAnalysisDataNoFamAdminBinary = SampledOPMDataAdmin.copy()
cols = ["GENDER",
"DATECODE",
"QTR",
"COUNT",
"AGYTYPT",
"AGYT",
"AGYSUB",
"AGYSUBT",
"QTR",
"AGELVLT",
"LOSLVL",
"LOSLVLT",
"LOCTYPT",
"LOCT",
"OCCTYP",
"OCCTYPT",
"OCCFAM",
"OCCFAMT",
"OCC",
"OCCT",
"PATCO",
"PPGRD",
"PATCOT",
"PPTYPT",
"PPGROUPT",
"PAYPLAN",
"PAYPLANT",
"SALLVLT",
"TOATYPT",
"TOAT",
"WSTYP",
"WSTYPT",
"WORKSCH",
"WORKSCHT",
"SALARY",
"LOS",
"SEPCount_EFDATE_OCC",
"SEPCount_EFDATE_LOC"
]
#delete cols from analysis data
for col in cols:
if col in list(OPMAnalysisDataNoFamAdminBinary.columns):
del OPMAnalysisDataNoFamAdminBinary[col]
OPMAnalysisDataNoFamAdminBinary.info()
cols = ["AGELVL",
"LOC",
"SALLVL",
"TOA",
"AGYTYP",
"AGY",
"LOCTYP",
"PPTYP",
"PPGROUP",
"TOATYP"
]
#Split Values for cols
for col in cols:
if col in list(OPMAnalysisDataNoFamAdminBinary.columns):
AttSplit = pd.get_dummies(OPMAnalysisDataNoFamAdminBinary[col],prefix=col)
display(AttSplit.head())
OPMAnalysisDataNoFamAdminBinary = pd.concat((OPMAnalysisDataNoFamAdminBinary,AttSplit),axis=1) # add back into the dataframe
del OPMAnalysisDataNoFamAdminBinary[col]
pickleObject(OPMAnalysisDataNoFamAdminBinary, "OPMAnalysisDataNoFamAdminBinary")
display(OPMAnalysisDataNoFamAdminBinary.head())
print("Number of Columns: ",len(OPMAnalysisDataNoFamAdminBinary.columns))
OPMAnalysisDataNoFamAdminBinary.info()
Found the File! Loading Pickle Now!
| SEP | GSEGRD | IndAvgSalary | SalaryOverUnderIndAvg | LowerLimitAge | YearsToRetirement | BLS_FEDERAL_OtherSep_Rate | BLS_FEDERAL_Quits_Rate | BLS_FEDERAL_TotalSep_Level | BLS_FEDERAL_JobOpenings_Rate | BLS_FEDERAL_OtherSep_Level | BLS_FEDERAL_Quits_Level | BLS_FEDERAL_JobOpenings_Level | BLS_FEDERAL_Layoffs_Rate | BLS_FEDERAL_Layoffs_Level | BLS_FEDERAL_TotalSep_Rate | SALARYLog | LOSSqrt | SEPCount_EFDATE_OCCLog | SEPCount_EFDATE_LOCLog | IndAvgSalaryLog | AGELVL_B | AGELVL_C | AGELVL_D | AGELVL_E | AGELVL_F | AGELVL_G | AGELVL_H | AGELVL_I | AGELVL_J | AGELVL_K | LOC_01 | LOC_02 | LOC_04 | LOC_05 | LOC_06 | LOC_08 | LOC_09 | LOC_10 | LOC_11 | LOC_12 | LOC_13 | LOC_15 | LOC_16 | LOC_17 | LOC_18 | LOC_19 | LOC_20 | LOC_21 | LOC_22 | LOC_23 | LOC_24 | LOC_25 | LOC_26 | LOC_27 | LOC_28 | LOC_29 | LOC_30 | LOC_31 | LOC_32 | LOC_33 | LOC_34 | LOC_35 | LOC_36 | LOC_37 | LOC_38 | LOC_39 | LOC_40 | LOC_41 | LOC_42 | LOC_44 | LOC_45 | LOC_46 | LOC_47 | LOC_48 | LOC_49 | LOC_50 | LOC_51 | LOC_53 | LOC_54 | LOC_55 | LOC_56 | TOA_10 | TOA_15 | TOA_20 | TOA_30 | TOA_32 | TOA_35 | TOA_38 | TOA_40 | TOA_42 | TOA_44 | TOA_45 | TOA_48 | LOCTYP_1 | PPTYP_1 | PPGROUP_11 | PPGROUP_12 | TOATYP_1 | TOATYP_2 | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | NS | 7.0 | 42440.609454 | -928.609454 | 20.0 | 37.0 | 0.4 | 0.4 | 34 | 2.1 | 10 | 11 | 58 | 0.5 | 13 | 1.2 | 10.633738 | 0.316228 | 4.174387 | 5.365976 | 10.655861 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 1 | 1 | 0 | 1 | 0 |
| 1 | NS | 9.0 | 59811.047401 | -373.047401 | 25.0 | 32.0 | 0.4 | 0.4 | 34 | 2.1 | 10 | 11 | 58 | 0.5 | 13 | 1.2 | 10.992689 | 2.469818 | 4.905275 | 6.265301 | 10.998946 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 1 | 1 | 0 | 1 | 0 |
| 2 | NS | 9.0 | 54720.390255 | 1115.609745 | 25.0 | 32.0 | 0.4 | 0.4 | 34 | 2.1 | 10 | 11 | 58 | 0.5 | 13 | 1.2 | 10.930174 | 1.183216 | 4.905275 | 5.117994 | 10.909992 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 1 | 1 | 0 | 1 | 0 |
| 3 | NS | 11.0 | 66557.448029 | -5431.448029 | 25.0 | 32.0 | 0.4 | 0.4 | 34 | 2.1 | 10 | 11 | 58 | 0.5 | 13 | 1.2 | 11.020693 | 2.144761 | 5.627621 | 6.745236 | 11.105821 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 1 | 1 | 0 | 1 | 0 |
| 4 | NS | 11.0 | 70834.469577 | -7743.469577 | 25.0 | 32.0 | 0.4 | 0.4 | 34 | 2.1 | 10 | 11 | 58 | 0.5 | 13 | 1.2 | 11.052333 | 1.673320 | 6.148468 | 6.827629 | 11.168101 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 1 | 1 | 0 | 1 | 0 |
Number of Columns: 100 <class 'pandas.core.frame.DataFrame'> RangeIndex: 14918 entries, 0 to 14917 Data columns (total 100 columns): SEP 14918 non-null object GSEGRD 14918 non-null float64 IndAvgSalary 14918 non-null float64 SalaryOverUnderIndAvg 14918 non-null float64 LowerLimitAge 14918 non-null float64 YearsToRetirement 14918 non-null float64 BLS_FEDERAL_OtherSep_Rate 14918 non-null float64 BLS_FEDERAL_Quits_Rate 14918 non-null float64 BLS_FEDERAL_TotalSep_Level 14918 non-null int64 BLS_FEDERAL_JobOpenings_Rate 14918 non-null float64 BLS_FEDERAL_OtherSep_Level 14918 non-null int64 BLS_FEDERAL_Quits_Level 14918 non-null int64 BLS_FEDERAL_JobOpenings_Level 14918 non-null int64 BLS_FEDERAL_Layoffs_Rate 14918 non-null float64 BLS_FEDERAL_Layoffs_Level 14918 non-null int64 BLS_FEDERAL_TotalSep_Rate 14918 non-null float64 SALARYLog 14918 non-null float64 LOSSqrt 14918 non-null float64 SEPCount_EFDATE_OCCLog 14918 non-null float64 SEPCount_EFDATE_LOCLog 14918 non-null float64 IndAvgSalaryLog 14918 non-null float64 AGELVL_B 14918 non-null uint8 AGELVL_C 14918 non-null uint8 AGELVL_D 14918 non-null uint8 AGELVL_E 14918 non-null uint8 AGELVL_F 14918 non-null uint8 AGELVL_G 14918 non-null uint8 AGELVL_H 14918 non-null uint8 AGELVL_I 14918 non-null uint8 AGELVL_J 14918 non-null uint8 AGELVL_K 14918 non-null uint8 LOC_01 14918 non-null uint8 LOC_02 14918 non-null uint8 LOC_04 14918 non-null uint8 LOC_05 14918 non-null uint8 LOC_06 14918 non-null uint8 LOC_08 14918 non-null uint8 LOC_09 14918 non-null uint8 LOC_10 14918 non-null uint8 LOC_11 14918 non-null uint8 LOC_12 14918 non-null uint8 LOC_13 14918 non-null uint8 LOC_15 14918 non-null uint8 LOC_16 14918 non-null uint8 LOC_17 14918 non-null uint8 LOC_18 14918 non-null uint8 LOC_19 14918 non-null uint8 LOC_20 14918 non-null uint8 LOC_21 14918 non-null uint8 LOC_22 14918 non-null uint8 LOC_23 14918 non-null uint8 LOC_24 14918 non-null uint8 LOC_25 14918 non-null uint8 LOC_26 14918 non-null uint8 LOC_27 14918 non-null uint8 LOC_28 14918 non-null uint8 LOC_29 14918 non-null uint8 LOC_30 14918 non-null uint8 LOC_31 14918 non-null uint8 LOC_32 14918 non-null uint8 LOC_33 14918 non-null uint8 LOC_34 14918 non-null uint8 LOC_35 14918 non-null uint8 LOC_36 14918 non-null uint8 LOC_37 14918 non-null uint8 LOC_38 14918 non-null uint8 LOC_39 14918 non-null uint8 LOC_40 14918 non-null uint8 LOC_41 14918 non-null uint8 LOC_42 14918 non-null uint8 LOC_44 14918 non-null uint8 LOC_45 14918 non-null uint8 LOC_46 14918 non-null uint8 LOC_47 14918 non-null uint8 LOC_48 14918 non-null uint8 LOC_49 14918 non-null uint8 LOC_50 14918 non-null uint8 LOC_51 14918 non-null uint8 LOC_53 14918 non-null uint8 LOC_54 14918 non-null uint8 LOC_55 14918 non-null uint8 LOC_56 14918 non-null uint8 TOA_10 14918 non-null uint8 TOA_15 14918 non-null uint8 TOA_20 14918 non-null uint8 TOA_30 14918 non-null uint8 TOA_32 14918 non-null uint8 TOA_35 14918 non-null uint8 TOA_38 14918 non-null uint8 TOA_40 14918 non-null uint8 TOA_42 14918 non-null uint8 TOA_44 14918 non-null uint8 TOA_45 14918 non-null uint8 TOA_48 14918 non-null uint8 LOCTYP_1 14918 non-null uint8 PPTYP_1 14918 non-null uint8 PPGROUP_11 14918 non-null uint8 PPGROUP_12 14918 non-null uint8 TOATYP_1 14918 non-null uint8 TOATYP_2 14918 non-null uint8 dtypes: float64(15), int64(5), object(1), uint8(79) memory usage: 3.5+ MB CPU times: user 94.2 ms, sys: 763 µs, total: 95 ms Wall time: 93.4 ms
if os.path.isfile(PickleJarPath+"/OPMAnalysisScalerFit.pkl"):
print("Found the File! Loading Pickle Now!")
OPMAnalysisScalerFit = unpickleObject("OPMAnalysisScalerFit")
Found the File! Loading Pickle Now!
#OPMAnalysisDataNoFamAdminBinary = OPMAnalysisDataNoFamAdmin[(OPMAnalysisDataNoFamAdmin["SEP"] == 'NS') | (OPMAnalysisDataNoFamAdmin["SEP"] == 'SC')].reset_index()
OPMAnalysisDataNoFamAdminBinaryScaled = OPMAnalysisDataNoFamAdminBinary[OPMScaledAnalysisData.columns]
print(OPMAnalysisDataNoFamAdminBinaryScaled.info())
<class 'pandas.core.frame.DataFrame'> RangeIndex: 14918 entries, 0 to 14917 Data columns (total 99 columns): GSEGRD 14918 non-null float64 IndAvgSalary 14918 non-null float64 SalaryOverUnderIndAvg 14918 non-null float64 LowerLimitAge 14918 non-null float64 YearsToRetirement 14918 non-null float64 BLS_FEDERAL_OtherSep_Rate 14918 non-null float64 BLS_FEDERAL_Quits_Rate 14918 non-null float64 BLS_FEDERAL_TotalSep_Level 14918 non-null int64 BLS_FEDERAL_JobOpenings_Rate 14918 non-null float64 BLS_FEDERAL_OtherSep_Level 14918 non-null int64 BLS_FEDERAL_Quits_Level 14918 non-null int64 BLS_FEDERAL_JobOpenings_Level 14918 non-null int64 BLS_FEDERAL_Layoffs_Rate 14918 non-null float64 BLS_FEDERAL_Layoffs_Level 14918 non-null int64 BLS_FEDERAL_TotalSep_Rate 14918 non-null float64 SALARYLog 14918 non-null float64 LOSSqrt 14918 non-null float64 SEPCount_EFDATE_OCCLog 14918 non-null float64 SEPCount_EFDATE_LOCLog 14918 non-null float64 IndAvgSalaryLog 14918 non-null float64 AGELVL_B 14918 non-null uint8 AGELVL_C 14918 non-null uint8 AGELVL_D 14918 non-null uint8 AGELVL_E 14918 non-null uint8 AGELVL_F 14918 non-null uint8 AGELVL_G 14918 non-null uint8 AGELVL_H 14918 non-null uint8 AGELVL_I 14918 non-null uint8 AGELVL_J 14918 non-null uint8 AGELVL_K 14918 non-null uint8 LOC_01 14918 non-null uint8 LOC_02 14918 non-null uint8 LOC_04 14918 non-null uint8 LOC_05 14918 non-null uint8 LOC_06 14918 non-null uint8 LOC_08 14918 non-null uint8 LOC_09 14918 non-null uint8 LOC_10 14918 non-null uint8 LOC_11 14918 non-null uint8 LOC_12 14918 non-null uint8 LOC_13 14918 non-null uint8 LOC_15 14918 non-null uint8 LOC_16 14918 non-null uint8 LOC_17 14918 non-null uint8 LOC_18 14918 non-null uint8 LOC_19 14918 non-null uint8 LOC_20 14918 non-null uint8 LOC_21 14918 non-null uint8 LOC_22 14918 non-null uint8 LOC_23 14918 non-null uint8 LOC_24 14918 non-null uint8 LOC_25 14918 non-null uint8 LOC_26 14918 non-null uint8 LOC_27 14918 non-null uint8 LOC_28 14918 non-null uint8 LOC_29 14918 non-null uint8 LOC_30 14918 non-null uint8 LOC_31 14918 non-null uint8 LOC_32 14918 non-null uint8 LOC_33 14918 non-null uint8 LOC_34 14918 non-null uint8 LOC_35 14918 non-null uint8 LOC_36 14918 non-null uint8 LOC_37 14918 non-null uint8 LOC_38 14918 non-null uint8 LOC_39 14918 non-null uint8 LOC_40 14918 non-null uint8 LOC_41 14918 non-null uint8 LOC_42 14918 non-null uint8 LOC_44 14918 non-null uint8 LOC_45 14918 non-null uint8 LOC_46 14918 non-null uint8 LOC_47 14918 non-null uint8 LOC_48 14918 non-null uint8 LOC_49 14918 non-null uint8 LOC_50 14918 non-null uint8 LOC_51 14918 non-null uint8 LOC_53 14918 non-null uint8 LOC_54 14918 non-null uint8 LOC_55 14918 non-null uint8 LOC_56 14918 non-null uint8 TOA_10 14918 non-null uint8 TOA_15 14918 non-null uint8 TOA_20 14918 non-null uint8 TOA_30 14918 non-null uint8 TOA_32 14918 non-null uint8 TOA_35 14918 non-null uint8 TOA_38 14918 non-null uint8 TOA_40 14918 non-null uint8 TOA_42 14918 non-null uint8 TOA_44 14918 non-null uint8 TOA_45 14918 non-null uint8 TOA_48 14918 non-null uint8 LOCTYP_1 14918 non-null uint8 PPTYP_1 14918 non-null uint8 PPGROUP_11 14918 non-null uint8 PPGROUP_12 14918 non-null uint8 TOATYP_1 14918 non-null uint8 TOATYP_2 14918 non-null uint8 dtypes: float64(15), int64(5), uint8(79) memory usage: 3.4 MB None
%%time
OPMAnalysisDataNoFamAdminBinaryScaled = pd.DataFrame(OPMAnalysisScalerFit.transform(OPMAnalysisDataNoFamAdminBinaryScaled), columns = OPMAnalysisDataNoFamAdminBinaryScaled.columns)
print("Overall Accuracy, predicting Admin Binary Separation from Professional Model: ", rfc_clf.score(OPMAnalysisDataNoFamAdminBinaryScaled[LRSigCols], np.where(OPMAnalysisDataNoFamAdminBinary["SEP"]=="NS",0,1)))
results = pd.concat([OPMAnalysisDataNoFamAdminBinary, pd.DataFrame({"Prediction": rfc_clf.predict(OPMAnalysisDataNoFamAdminBinaryScaled[LRSigCols])})], axis = 1)
results["SEPNum"] = np.where(results["SEP"]=="NS",0,1)
results["PredictTxt"] = np.where(results["Prediction"]==0,"NS","SC")
display(pd.DataFrame({'Cnt' : results.groupby(["SEP"]).size()}).reset_index())
display(pd.DataFrame({'Cnt' : results.groupby(["SEP", "PredictTxt"]).size()}).reset_index())
print("confusion matrix\n{0}\n".format(pd.crosstab(results.PredictTxt, results.SEP, rownames = ['True'], colnames = ['Predicted'], margins = True)))
# Plot non-normalized confusion matrix
plt.figure()
plot_confusion_matrixBinary(confusion_matrix(results.Prediction, results.SEPNum),
classes =["NS", "SC"],
normalize =True,
title ='Confusion matrix, with normalization')
Overall Accuracy, predicting Admin Binary Separation from Professional Model: 0.726571926532
| SEP | Cnt | |
|---|---|---|
| 0 | NS | 7495 |
| 1 | SC | 7423 |
| SEP | PredictTxt | Cnt | |
|---|---|---|---|
| 0 | NS | NS | 4858 |
| 1 | NS | SC | 2637 |
| 2 | SC | NS | 1442 |
| 3 | SC | SC | 5981 |
confusion matrix Predicted NS SC All True NS 4858 1442 6300 SC 2637 5981 8618 All 7495 7423 14918 Normalized confusion matrix [[ 0.77111111 0.22888889] [ 0.30598747 0.69401253]]
/usr/local/es7/lib/python3.5/site-packages/matplotlib/font_manager.py:1297: UserWarning: findfont: Font family ['sans-serif'] not found. Falling back to DejaVu Sans (prop.get_family(), self.defaultFamily[fontext]))
CPU times: user 640 ms, sys: 289 ms, total: 929 ms Wall time: 607 ms
%%time
print("Overall Accuracy, predicting Admin Binary Separation from Professional Model: ", knn_clf.score(OPMAnalysisDataNoFamAdminBinaryScaled[PCList], np.where(OPMAnalysisDataNoFamAdminBinary["SEP"]=="NS",0,1)))
results = pd.concat([OPMAnalysisDataNoFamAdminBinary, pd.DataFrame({"Prediction": knn_clf.predict(OPMAnalysisDataNoFamAdminBinaryScaled[PCList])})], axis = 1)
results["SEPNum"] = np.where(results["SEP"]=="NS",0,1)
results["PredictTxt"] = np.where(results["Prediction"]==0,"NS","SC")
display(pd.DataFrame({'Cnt' : results.groupby(["SEP"]).size()}).reset_index())
display(pd.DataFrame({'Cnt' : results.groupby(["SEP", "PredictTxt"]).size()}).reset_index())
print("confusion matrix\n{0}\n".format(pd.crosstab(results.PredictTxt, results.SEP, rownames = ['True'], colnames = ['Predicted'], margins = True)))
# Plot non-normalized confusion matrix
plt.figure()
plot_confusion_matrixBinary(confusion_matrix(results.Prediction, results.SEPNum),
classes =["NS", "SC"],
normalize =True,
title ='Confusion matrix, with normalization')
Overall Accuracy, predicting Admin Binary Separation from Professional Model: 0.709880681056
| SEP | Cnt | |
|---|---|---|
| 0 | NS | 7495 |
| 1 | SC | 7423 |
| SEP | PredictTxt | Cnt | |
|---|---|---|---|
| 0 | NS | NS | 5554 |
| 1 | NS | SC | 1941 |
| 2 | SC | NS | 2387 |
| 3 | SC | SC | 5036 |
confusion matrix Predicted NS SC All True NS 5554 2387 7941 SC 1941 5036 6977 All 7495 7423 14918 Normalized confusion matrix [[ 0.69940813 0.30059187] [ 0.2781998 0.7218002 ]]
/usr/local/es7/lib/python3.5/site-packages/matplotlib/font_manager.py:1297: UserWarning: findfont: Font family ['sans-serif'] not found. Falling back to DejaVu Sans (prop.get_family(), self.defaultFamily[fontext]))
CPU times: user 21min 8s, sys: 1.25 s, total: 21min 9s Wall time: 31.3 s
%%time
print("Overall Accuracy, predicting Admin Binary Separation from Professional Model: ", lr_clf.score(OPMAnalysisDataNoFamAdminBinaryScaled[LRSigCols], np.where(OPMAnalysisDataNoFamAdminBinary["SEP"]=="NS",0,1)))
results = pd.concat([OPMAnalysisDataNoFamAdminBinary, pd.DataFrame({"Prediction": lr_clf.predict(OPMAnalysisDataNoFamAdminBinaryScaled[LRSigCols])})], axis = 1)
results["SEPNum"] = np.where(results["SEP"]=="NS",0,1)
results["PredictTxt"] = np.where(results["Prediction"]==0,"NS","SC")
display(pd.DataFrame({'Cnt' : results.groupby(["SEP"]).size()}).reset_index())
display(pd.DataFrame({'Cnt' : results.groupby(["SEP", "PredictTxt"]).size()}).reset_index())
print("confusion matrix\n{0}\n".format(pd.crosstab(results.PredictTxt, results.SEP, rownames = ['True'], colnames = ['Predicted'], margins = True)))
# Plot non-normalized confusion matrix
plt.figure()
plot_confusion_matrixBinary(confusion_matrix(results.Prediction, results.SEPNum),
classes =["NS", "SC"],
normalize =True,
title ='Confusion matrix, with normalization')
Overall Accuracy, predicting Admin Binary Separation from Professional Model: 0.726303794074
| SEP | Cnt | |
|---|---|---|
| 0 | NS | 7495 |
| 1 | SC | 7423 |
| SEP | PredictTxt | Cnt | |
|---|---|---|---|
| 0 | NS | NS | 5269 |
| 1 | NS | SC | 2226 |
| 2 | SC | NS | 1857 |
| 3 | SC | SC | 5566 |
confusion matrix Predicted NS SC All True NS 5269 1857 7126 SC 2226 5566 7792 All 7495 7423 14918 Normalized confusion matrix [[ 0.739405 0.260595 ] [ 0.28567762 0.71432238]]
/usr/local/es7/lib/python3.5/site-packages/matplotlib/font_manager.py:1297: UserWarning: findfont: Font family ['sans-serif'] not found. Falling back to DejaVu Sans (prop.get_family(), self.defaultFamily[fontext]))
CPU times: user 1.12 s, sys: 4.64 s, total: 5.76 s Wall time: 394 ms